fabro/run.json

{
  "title": "Context Window Breakdown Endpoint Plan",
  "spec": {
    "run_id": "01KSB7GKM0A8P61YYCV7WNYJG9",
    "settings": {
      "project": {
        "name": null,
        "description": null,
        "metadata": {}
      },
      "workflow": {
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        "graph": "workflow.fabro",
        "metadata": {}
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      "run": {
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          "value": "# Context Window Breakdown Endpoint Plan\n\nDate: 2026-05-23\n\n## Context\n\nAgent stage pages already have enough projected data for todos, subagents,\nskills, and MCP servers through `StageProjection` in\n`lib/crates/fabro-types/src/run_projection.rs` and the reducer in\n`lib/crates/fabro-store/src/run_state.rs`. Context-window usage is different:\nFabro emits context-window warnings and compaction events today, but it does not\nstore a category breakdown of the model-visible request context.\n\n`fabro-llm` already exposes the right counting primitive:\n`Client::count_input_tokens(request, InputTokenCountPreference::PreferProvider)`\nin `lib/crates/fabro-llm/src/client.rs`. Provider adapters can call native count\nendpoints for OpenAI, Anthropic, and Gemini, and the client already falls back\nto local estimates for fallback-eligible failures.\n\n## Goal\n\nAdd a best-effort context-window API for agent stages:\n\n```text\nGET /api/v1/runs/{id}/stages/{stageId}/context-window\n```\n\nThe endpoint should return the best context-window usage Fabro can produce with\nno caller-controlled count or accuracy parameters. It may call the configured\nLLM provider by default. If provider counting is not possible, it should degrade\nto a local estimate or the latest stored snapshot instead of making the sidebar\ntreat ordinary count gaps as hard errors.\n\n## Scope\n\nIn scope:\n\n- OpenAPI contract and generated Rust/TypeScript clients.\n- Content-free context-window DTOs in the run projection.\n- A typed agent event for latest context-window snapshots.\n- Server endpoint that combines live provider counting, local estimates, and\n  stored-snapshot fallback.\n- Web query key, hook, and SSE invalidation support so the future sidebar can\n  consume the endpoint.\n\nOut of scope:\n\n- Building the full new left sidebar UI.\n- Persisting raw prompt, memory, tool arguments, or message contents for later\n  token counting.\n- Adding user-visible count-mode or accuracy knobs.\n- Retrofitting exact historical context-window counts for older completed runs.\n\nBefore implementing, read:\n\n- `docs/internal/events-strategy.md`\n- `docs/internal/error-handling-strategy.md`\n- `docs/internal/testing-strategy.md`\n\n## API Contract\n\nAdd the route under the existing Run Internals tag in\n`docs/public/api-reference/fabro-api.yaml`:\n\n```text\nGET /runs/{id}/stages/{stageId}/context-window\n```\n\nProposed response shape:\n\n```json\n{\n  \"stage_id\": \"implement@1\",\n  \"available\": true,\n  \"unavailable_reason\": null,\n  \"provider\": \"openai\",\n  \"model\": \"gpt-5.4\",\n  \"context_window_tokens\": 400000,\n  \"input_tokens\": 123456,\n  \"usage_percent\": 30.86,\n  \"count_method\": \"provider_api_scaled_breakdown\",\n  \"staleness\": \"live\",\n  \"generated_at\": \"2026-05-23T12:34:56Z\",\n  \"event_seq\": 42,\n  \"breakdown\": [\n    {\n      \"category\": \"system_prompt\",\n      \"label\": \"System prompt\",\n      \"tokens\": 30000,\n      \"usage_percent\": 7.5,\n      \"source\": \"scaled_local_estimate\"\n    }\n  ],\n  \"warnings\": []\n}\n```\n\nRequired schemas:\n\n- `StageContextWindow`\n- `StageContextWindowBreakdownItem`\n- `StageContextWindowCategory`\n- `StageContextWindowCountMethod`\n- `StageContextWindowStaleness`\n- `StageContextWindowUnavailableReason`\n- `StageContextWindowWarning`\n\nEnums:\n\n```text\nStageContextWindowCategory:\n  system_prompt\n  tools\n  mcp_tools\n  skills\n  memory\n  conversation\n  other\n\nStageContextWindowCountMethod:\n  provider_api_scaled_breakdown\n  response_usage_scaled_breakdown\n  local_estimate\n\nStageContextWindowStaleness:\n  live\n  stored\n  unavailable\n\nStageContextWindowUnavailableReason:\n  not_agent_stage\n  not_observed\n  provider_unconfigured\n```\n\nUse `available: false` for a real run/stage where Fabro has no context-window\ndata yet. Missing runs and missing stages should still return 404. For\n`available: false`, return `breakdown: []`, `warnings` explaining the gap, and\nnullable token fields.\n\nDefine context-window usage as model-visible input/context tokens only:\n\n- Include prompt input, system/developer content, tool definitions, MCP tool\n  definitions, skills, memory files, conversation history, tool results, and\n  cache input tokens when response usage is the source.\n- Exclude output tokens and reasoning tokens.\n- Do not report cost/billing totals here. Existing billing APIs own billing.\n\n## Data Model\n\nAdd projection-only, content-free context-window types to\n`lib/crates/fabro-types/src/run_projection.rs`:\n\n- `StageContextWindowProjection`\n- `StageContextWindowBreakdownProjection`\n- category/count/staleness/warning enums, shared with the API through\n  `fabro-api` replacements if the serde shape matches.\n\nExtend `StageProjection` with:\n\n```rust\n#[serde(default, skip_serializing_if = \"Option::is_none\")]\npub context_window: Option<StageContextWindowProjection>,\n```\n\nDo not store raw content. The stored snapshot may contain only:\n\n- provider and model\n- context window size\n- input token total\n- category token counts\n- count method\n- generated timestamp\n- source event sequence\n- warning codes and messages\n\n## Events\n\nAdd a typed event in `lib/crates/fabro-types/src/run_event/agent.rs` and\n`lib/crates/fabro-types/src/run_event/mod.rs`:\n\n```text\nagent.context_window.snapshot\n```\n\nEvent payload should carry the same content-free counts as the projection plus\nthe stage id. The reducer in `lib/crates/fabro-store/src/run_state.rs` should\nreplace the selected stage's `context_window` with the latest snapshot.\n\nThis event is the durable fallback for inactive stages. It should be emitted\nwhen the agent assembles or refreshes the LLM request context, before the\nprovider request is sent. If a provider response later supplies better input\nusage for the same request, emit another snapshot using\n`response_usage_scaled_breakdown`.\n\n## Live Counting Design\n\nThe agent should produce snapshots using this decision order whenever it builds\nan LLM request:\n\n1. Build a content-free local category breakdown from the same inputs used to\n   assemble the `fabro_llm::Request`.\n2. Emit an immediate `agent.context_window.snapshot` with `local_estimate` so a\n   sidebar has data even if provider counting is slow or unavailable.\n3. Attempt provider counting with\n   `Client::count_input_tokens(..., InputTokenCountPreference::PreferProvider)`\n   using the exact in-memory request. This work must not persist or log the raw\n   request.\n4. If provider count succeeds, scale the local category estimates to the\n   provider total and emit a replacement snapshot with\n   `provider_api_scaled_breakdown`.\n5. If provider count falls back or fails, keep the local snapshot and include a\n   warning code on the next emitted snapshot. Count failures must not block the\n   agent's normal LLM request.\n\nThe API endpoint should follow this decision order:\n\n1. Validate the run and stage exist.\n2. If the stage is not an agent stage, return `available: false` with\n   `unavailable_reason: not_agent_stage`.\n3. Return the latest projected `StageContextWindowProjection`.\n4. If no snapshot has ever been observed, return `available: false` with\n   `unavailable_reason: not_observed`.\n\nDo not make the HTTP server own raw LLM requests. The existing server state\ntracks live run control and durable projections, while the exact request exists\ninside the active agent session. Provider counting should therefore happen in\nthe agent/worker process at request-assembly time, and the server endpoint\nshould expose the latest durable snapshot.\n\nIf a future implementation needs user-triggered refreshes, add a separate\nworker request/response control path. Do not tunnel raw request content through\nrun events or store it in `ManagedRun`.\n\nThe live request snapshot needs to be short-lived and content-safe:\n\n- Hold raw `Request` content only inside active agent sessions.\n- Never write that raw request to run events, projection state, logs, or API\n  responses.\n- Cache provider-count results by run id, stage id, provider, model, and request\n  fingerprint or source event seq so each LLM request is counted at most once.\n- Clear the live request handle when the session/stage deactivates.\n\n## Resolved Handoff Decisions\n\n### Decision 1: Category-Aware Estimation Ownership\n\nOptions:\n\n- Agent-only estimator: build all category counts in `fabro-agent`.\n- LLM-only estimator: move the full breakdown model into `fabro-llm`.\n- Hybrid estimator: keep category ownership in `fabro-agent`, but expose small\n  reusable token-estimation helpers from `fabro-llm`.\n\nRecommendation: use the hybrid estimator.\n\nJustification:\n\n- `fabro-agent` has the category knowledge. It sees memory documents, skills,\n  MCP registration, tool registry policy, and the final session history before\n  `Session::build_request` flattens everything into a generic LLM request.\n- `fabro-llm` has the token math and provider-neutral request structures. It\n  already owns local count behavior in `token_count.rs`, so duplicating that\n  estimator in `fabro-agent` would drift.\n- `fabro-llm` should not learn Fabro-specific categories like `skills` or\n  `memory`; that would couple a provider abstraction crate to agent UI\n  semantics.\n\nImplementation guidance:\n\n- Add a `fabro-agent/src/context_window.rs` builder that owns the category\n  taxonomy and content-free snapshot assembly.\n- Expose narrow helpers from `fabro-llm::token_count`, such as tool-definition\n  and message/content-part estimators, instead of making the private estimator\n  logic public wholesale.\n- Keep the existing `Client::count_input_tokens` provider call as the\n  authoritative total when available.\n\n### Decision 2: Provider Count Location\n\nOptions:\n\n- Server-side count: store or reconstruct the exact `fabro_llm::Request` in the\n  HTTP server and call the provider from the endpoint.\n- Synchronous worker query: add a request/response control channel so the HTTP\n  server can ask the active worker for a fresh count on demand.\n- Agent-side count on request assembly: the active session counts the exact\n  request it already has and emits content-free snapshots; the endpoint returns\n  the latest projection.\n\nRecommendation: use agent-side count on request assembly for the first\nimplementation.\n\nJustification:\n\n- It satisfies the requirement that provider counting is attempted by default,\n  because every active agent request can be counted once as it is assembled.\n- It avoids moving raw prompts, memory, tool results, or message history into\n  server-managed state.\n- The existing subprocess control path is one-way JSONL for actions like steer,\n  interrupt, and pair. Adding a synchronous query path just for this read model\n  would be more complex than emitting the durable projection the UI already\n  needs.\n- It works for both subprocess and in-process runs because the agent session is\n  the common place where the exact request exists.\n\nImplementation guidance:\n\n- Emit a local snapshot immediately, then emit a provider-scaled replacement if\n  provider counting succeeds.\n- Do not delay the LLM stream on provider counting unless implementation finds\n  that provider count latency is consistently negligible. A spawned count task\n  with the cloned request is acceptable as long as it is cancelled/ignored when\n  the session closes.\n- Treat provider count errors as snapshot warnings, not stage failures.\n\n### Decision 3: Memory and Skills Attribution\n\nOptions:\n\n- Keep the current flattened system prompt and count all prompt additions as\n  `system_prompt`.\n- Refactor prompt assembly to retain component boundaries, then estimate\n  memory and skills before the final prompt string is concatenated.\n- Add origin metadata to every history message and tool result so activated\n  skill instructions can be attributed even after entering the conversation.\n\nRecommendation: refactor prompt assembly for this first slice; defer full\nhistory-origin metadata.\n\nJustification:\n\n- `assemble_system_prompt` already receives `memory` and `skills` separately,\n  then concatenates them with the core prompt. Returning component metadata from\n  that boundary is a small, local change.\n- This gives useful and accurate first-slice attribution for loaded memory\n  files and the available-skills prompt without changing the persisted\n  conversation format.\n- Activated skill instructions are harder: slash expansion becomes a user turn,\n  and `use_skill` returns a tool result. The current `Message` enum does not\n  preserve source metadata. Adding it is possible, but it is a broader history\n  serialization migration and should not block the endpoint.\n\nImplementation guidance:\n\n- Count the base/core prompt as `system_prompt`.\n- Count memory document text appended by prompt assembly as `memory`.\n- Count the available-skills section and the `use_skill` tool definition as\n  `skills`.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` in this first implementation, with a warning such as\n  `activated_skill_context_counted_as_conversation` when such activations are\n  present.\n\n### Decision 4: Tool vs MCP Tool Attribution\n\nOptions:\n\n- Split MCP tools by name prefix, such as `mcp__`.\n- Add source metadata to `RegisteredTool` / `ToolRegistry`.\n- Recompute MCP membership from `McpConnectionManager` at request time.\n\nRecommendation: add source metadata to `RegisteredTool` / `ToolRegistry`.\n\nJustification:\n\n- The current registry stores only `ToolDefinition` plus executor, so origin is\n  lost after registration.\n- Prefix-based classification matches today's naming convention but is brittle\n  and will misclassify any future native tool that shares the prefix or any MCP\n  naming change.\n- `McpConnectionManager` has source knowledge during registration, but the\n  request builder only sees the final registry and policy-filtered tool\n  definitions.\n\nImplementation guidance:\n\n- Add a small `ToolSource` enum, for example `Native`, `Mcp { server_name }`,\n  and `Skill`.\n- Set `ToolSource::Mcp` in `mcp_integration::make_mcp_tools`.\n- Set `ToolSource::Skill` for `make_use_skill_tool`.\n- Keep existing public `definitions()` behavior unchanged; add a parallel\n  method that returns definitions with source metadata for context-window\n  accounting.\n\n### Decision 5: Unavailable and Error Semantics\n\nOptions:\n\n- Return 404/409 for non-agent stages or stages with no snapshot.\n- Return 200 with `available: false` for known stages where context-window data\n  is not applicable or not observed.\n- Return partial data with warnings for provider-count failures.\n\nRecommendation: return 200 with `available: false` for known-but-unavailable\ndata, and reserve HTTP errors for missing run/stage or malformed requests.\n\nJustification:\n\n- The sidebar needs to render stable empty states without treating normal\n  projection gaps as transport errors.\n- Provider-count support varies by provider and credentials; those are data\n  quality issues, not endpoint availability issues.\n- This matches the broader best-effort contract and avoids UI retry loops when\n  a completed older run simply has no context-window snapshot.\n\nImplementation guidance:\n\n- Use 404 only for missing run or missing stage.\n- Use `available: false` + `unavailable_reason` for `not_agent_stage`,\n  `not_observed`, or `provider_unconfigured`.\n- Use `warnings` for local estimate, provider fallback, ambiguous categories,\n  and activated skill content counted as conversation.\n\nThe previous open questions are resolved by these decisions.\n\n## Implementation Units\n\n### Unit 1: OpenAPI and Generated Clients\n\nFiles:\n\n- `docs/public/api-reference/fabro-api.yaml`\n- `lib/crates/fabro-api/build.rs`\n- `lib/crates/fabro-api/tests/`\n- `lib/packages/fabro-api-client/`\n\nTasks:\n\n- Add the path and schemas under Run Internals.\n- Prefer reusing hand-written Rust projection types through\n  `with_replacement(...)` when serde shape and semantics are identical.\n- Add a `fabro-api` test proving type identity and JSON parity for any new\n  replacement.\n- Regenerate Rust and TypeScript clients.\n\nTests:\n\n- `cargo build -p fabro-api`\n- `cargo nextest run -p fabro-api`\n- `cd lib/packages/fabro-api-client && bun run generate`\n\n### Unit 2: Content-Free Snapshot Builder\n\nFiles:\n\n- `lib/crates/fabro-llm/src/token_count.rs`\n- `lib/crates/fabro-agent/src/context_window.rs` (new)\n- `lib/crates/fabro-agent/src/session.rs`\n- `lib/crates/fabro-agent/src/profiles/mod.rs`\n- `lib/crates/fabro-agent/src/tool_registry.rs`\n- `lib/crates/fabro-agent/src/mcp_integration.rs`\n- `lib/crates/fabro-agent/src/skills.rs`\n- `lib/crates/fabro-agent/src/compaction.rs`\n- `lib/crates/fabro-agent/src/lib.rs`\n\nTasks:\n\n- Build category estimates at the same boundary where `Session::build_request`\n  assembles the `fabro_llm::Request`.\n- Expose narrow reusable token-estimation helpers from `fabro-llm` rather than\n  duplicating the estimator in `fabro-agent`.\n- Refactor prompt assembly enough to retain component boundaries for core\n  system prompt, memory, available skills, and user instructions before the\n  final prompt string is concatenated.\n- Add source metadata to registered tools so the builder can split native tools,\n  MCP tools, and skill-related tools after policy filtering.\n- Classify the system prompt separately from conversation history.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` for the first implementation, with a warning when activated\n  skill context is present.\n- Emit a local snapshot immediately and a provider-scaled replacement snapshot\n  when provider counting succeeds.\n- Keep local estimates deterministic and content-free.\n\nTests:\n\n- system prompt, tools, MCP tools, skills, memory, conversation, and other\n  categories are counted into the expected buckets.\n- category totals equal the local total before provider scaling.\n- provider-scaled totals add up to the provider total.\n- snapshots do not serialize prompt text, memory contents, tool arguments, or\n  message text.\n- opaque/ambiguous inputs produce warnings rather than silent misclassification.\n- provider count failure does not fail the agent turn.\n- each request fingerprint is counted by the provider at most once.\n\n### Unit 3: Event and Projection\n\nFiles:\n\n- `lib/crates/fabro-types/src/run_event/agent.rs`\n- `lib/crates/fabro-types/src/run_event/mod.rs`\n- `lib/crates/fabro-types/src/run_projection.rs`\n- `lib/crates/fabro-store/src/run_state.rs`\n\nTasks:\n\n- Add `agent.context_window.snapshot`.\n- Emit snapshots from the agent session path when requests are assembled and\n  when later response usage improves the count.\n- Project the latest snapshot onto `StageProjection.context_window`.\n- Preserve backwards-compatible deserialization for run projections that do not\n  have the new field.\n\nTests:\n\n- event `type_name()` returns `agent.context_window.snapshot`.\n- reducer updates only the matching stage.\n- later snapshots replace earlier snapshots for the same stage.\n- old projection JSON without `context_window` still deserializes.\n\n### Unit 4: Server Endpoint\n\nFiles:\n\n- `lib/crates/fabro-server/src/server/handler/mod.rs`\n- `lib/crates/fabro-server/src/server/handler/runs.rs` or a new\n  `context_window.rs` handler module\n- `lib/crates/fabro-server/src/server/tests.rs`\n\nTasks:\n\n- Add `GET /runs/{id}/stages/{stageId}/context-window`.\n- Use the same run-scoped authorization pattern as adjacent run internals.\n- Resolve run/stage from the cached projection first for fast 404s and stored\n  fallback.\n- Return the latest projected context-window snapshot for the stage.\n- Return `available: false` for known stages where context-window data is not\n  applicable or has not been observed.\n- Return 404 only for missing run/stage. Provider count failures are represented\n  as snapshot warnings because provider counting happens in the agent.\n\nTests:\n\n- missing run returns 404.\n- missing stage returns 404.\n- non-agent stage returns 200 with `available: false`.\n- projected provider-count success returns `staleness: live` and\n  `count_method: provider_api_scaled_breakdown`.\n- inactive stage returns the latest stored projection snapshot.\n- no observed snapshot returns `available: false` with `not_observed`.\n- projected warning payloads are returned without changing HTTP status.\n\n### Unit 5: Web Query Support\n\nFiles:\n\n- `apps/fabro-web/app/lib/query-keys.ts`\n- `apps/fabro-web/app/lib/queries.ts`\n- `apps/fabro-web/app/lib/run-events.ts`\n- `apps/fabro-web/app/lib/query-keys.test.ts`\n- `apps/fabro-web/app/lib/run-events.test.tsx`\n\nTasks:\n\n- Add `queryKeys.runs.stageContextWindow(id, stageId)`.\n- Add `useRunStageContextWindow(runId, stageId)` using the generated\n  TypeScript client.\n- Invalidate the context-window key for:\n  - `agent.context_window.snapshot`\n  - stage lifecycle events for the same stage\n  - agent activity events that can change the request context\n- Keep the full sidebar UI as follow-up work, but make the hook ready for the\n  agent-node page.\n\nTests:\n\n- query key encodes run id and stage id stably.\n- snapshot event invalidates the context-window key, run events, and stage\n  events.\n- stage lifecycle events invalidate the context-window key for the selected\n  stage.\n- activity events without a stage id do not invalidate unrelated stage context\n  windows.\n\n## Security and Privacy\n\n- Do not persist raw request content to make inactive-stage provider counting\n  possible.\n- Do not log prompt, memory, tool args, or message contents while computing\n  counts.\n- Warning messages should identify count quality, not repeat provider error\n  bodies if those bodies may contain request excerpts.\n- The endpoint should expose counts and category labels only.\n\n## Validation\n\nExpected validation after implementation:\n\n```bash\ncargo build -p fabro-api\ncargo nextest run -p fabro-api -p fabro-agent -p fabro-store -p fabro-server\ncd apps/fabro-web && bun test\ncd apps/fabro-web && bun run typecheck\ncargo +nightly-2026-04-14 fmt --check --all\ngit diff --check\n```\n\n## Remaining Follow-Ups\n\n- Full sidebar visualization.\n- Optional history-origin metadata if we later want activated skill templates\n  to move from `conversation` into `skills`.\n- Optional user-triggered live refresh path if future product needs require\n  provider counting on demand rather than at request assembly time.\n"
        },
        "working_dir": null,
        "metadata": {},
        "inputs": {},
        "model": {
          "provider": "anthropic",
          "name": "claude-sonnet-4-6",
          "fallbacks": [],
          "controls": {
            "reasoning_effort": null,
            "speed": null
          }
        },
        "git": {
          "author": null
        },
        "prepare": {
          "commands": [],
          "timeout_ms": 300000
        },
        "execution": {
          "mode": "normal",
          "approval": "prompt"
        },
        "checkpoint": {
          "exclude_globs": [],
          "skip_git_hooks": false
        },
        "clone": {
          "enabled": true
        },
        "run_branch": {
          "enabled": true,
          "push": true
        },
        "meta_branch": {
          "enabled": true,
          "push": true
        },
        "environment": {
          "id": "fabro-dev",
          "provider": "daytona",
          "image": {
            "ref": "fabro-v11",
            "dockerfile": {
              "type": "inline",
              "value": "FROM ubuntu:24.04\n\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n        curl git ca-certificates build-essential pkg-config libssl-dev unzip python3 \\\n        xvfb xfce4 xfce4-terminal x11vnc novnc dbus-x11 \\\n        libx11-6 libxrandr2 libxext6 libxrender1 libxfixes3 libxss1 libxtst6 libxi6 \\\n    && rm -rf /var/lib/apt/lists/*\n\n# Install real Chromium (not the snap stub) via xtradeb PPA\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n        software-properties-common curl gnupg \\\n    && add-apt-repository -y ppa:xtradeb/apps \\\n    && apt-get update \\\n    && apt-get install -y --no-install-recommends chromium \\\n    && rm -rf /var/lib/apt/lists/*\n\n# Wrapper: Chromium needs --no-sandbox when running as root in a container,\n# and --disable-dev-shm-usage avoids crashes from small /dev/shm\nRUN printf '#!/bin/bash\\nexec /usr/bin/chromium --no-sandbox --disable-dev-shm-usage \"$@\"\\n' \\\n        > /usr/local/bin/chromium-wrapper \\\n    && chmod +x /usr/local/bin/chromium-wrapper\n\n# Make the wrapper the default in the system .desktop file and via alternatives\nRUN sed -i 's|^Exec=.*|Exec=/usr/local/bin/chromium-wrapper %U|' \\\n        /usr/share/applications/chromium.desktop \\\n    && update-alternatives --install /usr/bin/x-www-browser x-www-browser \\\n        /usr/local/bin/chromium-wrapper 100\n\n# Tell XFCE's exo-open that Chromium is the WebBrowser helper (system-wide)\nRUN mkdir -p /etc/xdg/xfce4 /usr/share/xfce4/helpers \\\n    && printf 'WebBrowser=custom-WebBrowser\\n' > /etc/xdg/xfce4/helpers.rc \\\n    && printf '[Desktop Entry]\\n\\\nVersion=1.0\\n\\\nType=X-XFCE-Helper\\n\\\nName=Chromium\\n\\\nIcon=chromium\\n\\\nX-XFCE-Category=WebBrowser\\n\\\nX-XFCE-CommandsWithParameter=/usr/local/bin/chromium-wrapper \"%%s\"\\n\\\nX-XFCE-Commands=/usr/local/bin/chromium-wrapper\\n' \\\n        > /usr/share/xfce4/helpers/custom-WebBrowser.desktop\n\n# GitHub CLI\nRUN curl -fsSL https://cli.github.com/packages/githubcli-archive-keyring.gpg \\\n        | dd of=/usr/share/keyrings/githubcli-archive-keyring.gpg \\\n    && echo \"deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main\" \\\n        | tee /etc/apt/sources.list.d/github-cli.list > /dev/null \\\n    && apt-get update && apt-get install -y --no-install-recommends gh \\\n    && rm -rf /var/lib/apt/lists/*\n\n# Rust\nRUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y\nENV PATH=\"/root/.cargo/bin:${PATH}\"\nRUN rustup toolchain install nightly-2026-04-14 --profile minimal --component clippy,rustfmt\nRUN cargo install cargo-nextest --locked\nENV CARGO_INCREMENTAL=0\n\n# Bun\nRUN curl -fsSL https://bun.sh/install | bash\nENV PATH=\"/root/.bun/bin:${PATH}\"\n\nWORKDIR /root\n"
            }
          },
          "resources": {
            "cpu": 8,
            "memory": "16GB",
            "disk": "20GB"
          },
          "network": {
            "mode": "allow_all",
            "allow": []
          },
          "lifecycle": {
            "preserve": false,
            "stop_on_terminal": true,
            "auto_stop": "30m"
          },
          "labels": {
            "repo": "fabro-sh/fabro"
          },
          "volumes": [],
          "env": {}
        },
        "notifications": {},
        "interviews": {
          "provider": null,
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          "id": "fix_lints",
          "attrs": {
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              "String": "The preflight lint step failed. Read the build output from context and fix all clippy lint warnings."
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              "String": "anthropic"
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            "label": {
              "String": "Fix Lints"
            },
            "max_visits": {
              "Integer": 3
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              "String": "claude-opus-4-7"
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            "label": {
              "String": "Toolchain"
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            "script": {
              "String": "command -v cargo >/dev/null || { curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y && sudo ln -sf $HOME/.cargo/bin/* /usr/local/bin/; }; cargo --version 2>&1"
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              "Integer": 0
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              "String": "anthropic"
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              "String": "Simplify (Opus)"
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            "prompt": {
              "String": "# Simplify: Code Review and Cleanup\n\nReview changes vs. origin for reuse, quality, and efficiency. Fix any issues found.\n\n## Phase 1: Identify Changes\n\nRun git diff (or git diff HEAD if there are staged changes) to see what changed. If there are no git changes, review the most recently modified files that the user mentioned or that you edited earlier in this conversation.\n\n## Phase 2: Launch Three Review Agents in Parallel\n\nUse the Agent tool to launch all three agents concurrently in a single message. Pass each agent the full diff so it has the complete context.\n\n### Agent 1: Code Reuse Review\n\nFor each change:\n\n1. Search for existing utilities and helpers that could replace newly written code. Use Grep to find similar patterns elsewhere in the codebase — common locations are utility directories, shared modules, and files adjacent to the changed ones.\n2. Flag any new function that duplicates existing functionality. Suggest the existing function to use instead.\n3. Flag any inline logic that could use an existing utility — hand-rolled string manipulation, manual path handling, custom environment checks, ad-hoc type guards, and similar patterns are common candidates.\n\nNote: This is a greenfield app, so focus on maximizing simplicity and don't worry about changing things to achieve it.\n\n### Agent 2: Code Quality Review\n\nReview the same changes for hacky patterns:\n\n1. Redundant state: state that duplicates existing state, cached values that could be derived, observers/effects that could be direct calls\n2. Parameter sprawl: adding new parameters to a function instead of generalizing or restructuring existing ones\n3. Copy-paste with slight variation: near-duplicate code blocks that should be unified with a shared abstraction\n4. Leaky abstractions: exposing internal details that should be encapsulated, or breaking existing abstraction boundaries\n5. Stringly-typed code: using raw strings where constants, enums (string unions), or branded types already exist in the codebase\n\nNote: This is a greenfield app, so be aggressive in optimizing quality.\n\n### Agent 3: Efficiency Review\n\nReview the same changes for efficiency:\n\n1. Unnecessary work: redundant computations, repeated file reads, duplicate network/API calls, N+1 patterns\n2. Missed concurrency: independent operations run sequentially when they could run in parallel\n3. Hot-path bloat: new blocking work added to startup or per-request/per-render hot paths\n4. Unnecessary existence checks: pre-checking file/resource existence before operating (TOCTOU anti-pattern) — operate directly and handle the error\n5. Memory: unbounded data structures, missing cleanup, event listener leaks\n6. Overly broad operations: reading entire files when only a portion is needed, loading all items when filtering for one\n\n## Phase 3: Fix Issues\n\nWait for all three agents to complete. Aggregate their findings and fix each issue directly. If a finding is a false positive or not worth addressing, note it and move on — do not argue with the finding, just skip it.\n\nWhen done, briefly summarize what was fixed (or confirm the code was already clean)."
            }
          }
        },
        "implement": {
          "id": "implement",
          "attrs": {
            "label": {
              "String": "Implement"
            },
            "prompt": {
              "String": "Read the plan file referenced in the goal and implement every step. Make all the code changes described in the plan. Use red/green TDD."
            },
            "model": {
              "String": "gpt-5.5"
            },
            "reasoning_effort": {
              "String": "xhigh"
            },
            "provider": {
              "String": "openai"
            }
          }
        },
        "simplify_gpt": {
          "id": "simplify_gpt",
          "attrs": {
            "prompt": {
              "String": "# Simplify: Code Review and Cleanup\n\nReview changes vs. origin for reuse, quality, and efficiency. Fix any issues found.\n\n## Phase 1: Identify Changes\n\nRun git diff (or git diff HEAD if there are staged changes) to see what changed. If there are no git changes, review the most recently modified files that the user mentioned or that you edited earlier in this conversation.\n\n## Phase 2: Launch Three Review Agents in Parallel\n\nUse the Agent tool to launch all three agents concurrently in a single message. Pass each agent the full diff so it has the complete context.\n\n### Agent 1: Code Reuse Review\n\nFor each change:\n\n1. Search for existing utilities and helpers that could replace newly written code. Use Grep to find similar patterns elsewhere in the codebase — common locations are utility directories, shared modules, and files adjacent to the changed ones.\n2. Flag any new function that duplicates existing functionality. Suggest the existing function to use instead.\n3. Flag any inline logic that could use an existing utility — hand-rolled string manipulation, manual path handling, custom environment checks, ad-hoc type guards, and similar patterns are common candidates.\n\nNote: This is a greenfield app, so focus on maximizing simplicity and don't worry about changing things to achieve it.\n\n### Agent 2: Code Quality Review\n\nReview the same changes for hacky patterns:\n\n1. Redundant state: state that duplicates existing state, cached values that could be derived, observers/effects that could be direct calls\n2. Parameter sprawl: adding new parameters to a function instead of generalizing or restructuring existing ones\n3. Copy-paste with slight variation: near-duplicate code blocks that should be unified with a shared abstraction\n4. Leaky abstractions: exposing internal details that should be encapsulated, or breaking existing abstraction boundaries\n5. Stringly-typed code: using raw strings where constants, enums (string unions), or branded types already exist in the codebase\n\nNote: This is a greenfield app, so be aggressive in optimizing quality.\n\n### Agent 3: Efficiency Review\n\nReview the same changes for efficiency:\n\n1. Unnecessary work: redundant computations, repeated file reads, duplicate network/API calls, N+1 patterns\n2. Missed concurrency: independent operations run sequentially when they could run in parallel\n3. Hot-path bloat: new blocking work added to startup or per-request/per-render hot paths\n4. Unnecessary existence checks: pre-checking file/resource existence before operating (TOCTOU anti-pattern) — operate directly and handle the error\n5. Memory: unbounded data structures, missing cleanup, event listener leaks\n6. Overly broad operations: reading entire files when only a portion is needed, loading all items when filtering for one\n\n## Phase 3: Fix Issues\n\nWait for all three agents to complete. Aggregate their findings and fix each issue directly. If a finding is a false positive or not worth addressing, note it and move on — do not argue with the finding, just skip it.\n\nWhen done, briefly summarize what was fixed (or confirm the code was already clean)."
            },
            "provider": {
              "String": "openai"
            },
            "model": {
              "String": "gpt-5.5"
            },
            "label": {
              "String": "Simplify (GPT-55)"
            }
          }
        },
        "exit": {
          "id": "exit",
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              "String": "claude-opus-4-7"
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              "String": "anthropic"
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            "shape": {
              "String": "Msquare"
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            "label": {
              "String": "Exit"
            }
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        },
        "verify": {
          "id": "verify",
          "attrs": {
            "goal_gate": {
              "Boolean": true
            },
            "retry_target": {
              "String": "fixup"
            },
            "shape": {
              "String": "parallelogram"
            },
            "provider": {
              "String": "anthropic"
            },
            "model": {
              "String": "claude-opus-4-7"
            },
            "label": {
              "String": "Verify"
            },
            "script": {
              "String": "git fetch origin main 2>&1 && git merge --no-edit --no-stat origin/main 2>&1 && cargo +nightly-2026-04-14 fmt --all 2>&1 && cargo dev docs refresh 2>&1 && cargo +nightly-2026-04-14 fmt --check --all 2>&1 && ! rg -n 'AuthMode::Disabled|RunAuthMethod|RunSubjectProvenance|\\bActorRef\\b|\\bActorKind\\b|AuthenticatedSubject|AuthenticatedService|AuthorizeRunScoped|AuthorizeRunBlob|AuthorizeStageArtifact|AuthorizeCommandLog|auth_method\\s*==\\s*\"disabled\"' lib/crates apps lib/packages docs/public/api-reference/fabro-api.yaml 2>&1 && cargo +nightly-2026-04-14 clippy --workspace --all-targets -- -D warnings 2>&1 && cargo nextest run --workspace --status-level slow --profile ci 2>&1 && cargo dev docs check 2>&1 && bun install --frozen-lockfile 2>&1 && (cd apps/fabro-web && bun run typecheck) 2>&1 && (cd apps/fabro-web && bun run test) 2>&1 && (cd lib/packages/fabro-api-client && bun run typecheck) 2>&1 && cargo dev build -- -p fabro-cli --release 2>&1"
            }
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        "start": {
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        "goal": {
          "String": "# Context Window Breakdown Endpoint Plan\n\nDate: 2026-05-23\n\n## Context\n\nAgent stage pages already have enough projected data for todos, subagents,\nskills, and MCP servers through `StageProjection` in\n`lib/crates/fabro-types/src/run_projection.rs` and the reducer in\n`lib/crates/fabro-store/src/run_state.rs`. Context-window usage is different:\nFabro emits context-window warnings and compaction events today, but it does not\nstore a category breakdown of the model-visible request context.\n\n`fabro-llm` already exposes the right counting primitive:\n`Client::count_input_tokens(request, InputTokenCountPreference::PreferProvider)`\nin `lib/crates/fabro-llm/src/client.rs`. Provider adapters can call native count\nendpoints for OpenAI, Anthropic, and Gemini, and the client already falls back\nto local estimates for fallback-eligible failures.\n\n## Goal\n\nAdd a best-effort context-window API for agent stages:\n\n```text\nGET /api/v1/runs/{id}/stages/{stageId}/context-window\n```\n\nThe endpoint should return the best context-window usage Fabro can produce with\nno caller-controlled count or accuracy parameters. It may call the configured\nLLM provider by default. If provider counting is not possible, it should degrade\nto a local estimate or the latest stored snapshot instead of making the sidebar\ntreat ordinary count gaps as hard errors.\n\n## Scope\n\nIn scope:\n\n- OpenAPI contract and generated Rust/TypeScript clients.\n- Content-free context-window DTOs in the run projection.\n- A typed agent event for latest context-window snapshots.\n- Server endpoint that combines live provider counting, local estimates, and\n  stored-snapshot fallback.\n- Web query key, hook, and SSE invalidation support so the future sidebar can\n  consume the endpoint.\n\nOut of scope:\n\n- Building the full new left sidebar UI.\n- Persisting raw prompt, memory, tool arguments, or message contents for later\n  token counting.\n- Adding user-visible count-mode or accuracy knobs.\n- Retrofitting exact historical context-window counts for older completed runs.\n\nBefore implementing, read:\n\n- `docs/internal/events-strategy.md`\n- `docs/internal/error-handling-strategy.md`\n- `docs/internal/testing-strategy.md`\n\n## API Contract\n\nAdd the route under the existing Run Internals tag in\n`docs/public/api-reference/fabro-api.yaml`:\n\n```text\nGET /runs/{id}/stages/{stageId}/context-window\n```\n\nProposed response shape:\n\n```json\n{\n  \"stage_id\": \"implement@1\",\n  \"available\": true,\n  \"unavailable_reason\": null,\n  \"provider\": \"openai\",\n  \"model\": \"gpt-5.4\",\n  \"context_window_tokens\": 400000,\n  \"input_tokens\": 123456,\n  \"usage_percent\": 30.86,\n  \"count_method\": \"provider_api_scaled_breakdown\",\n  \"staleness\": \"live\",\n  \"generated_at\": \"2026-05-23T12:34:56Z\",\n  \"event_seq\": 42,\n  \"breakdown\": [\n    {\n      \"category\": \"system_prompt\",\n      \"label\": \"System prompt\",\n      \"tokens\": 30000,\n      \"usage_percent\": 7.5,\n      \"source\": \"scaled_local_estimate\"\n    }\n  ],\n  \"warnings\": []\n}\n```\n\nRequired schemas:\n\n- `StageContextWindow`\n- `StageContextWindowBreakdownItem`\n- `StageContextWindowCategory`\n- `StageContextWindowCountMethod`\n- `StageContextWindowStaleness`\n- `StageContextWindowUnavailableReason`\n- `StageContextWindowWarning`\n\nEnums:\n\n```text\nStageContextWindowCategory:\n  system_prompt\n  tools\n  mcp_tools\n  skills\n  memory\n  conversation\n  other\n\nStageContextWindowCountMethod:\n  provider_api_scaled_breakdown\n  response_usage_scaled_breakdown\n  local_estimate\n\nStageContextWindowStaleness:\n  live\n  stored\n  unavailable\n\nStageContextWindowUnavailableReason:\n  not_agent_stage\n  not_observed\n  provider_unconfigured\n```\n\nUse `available: false` for a real run/stage where Fabro has no context-window\ndata yet. Missing runs and missing stages should still return 404. For\n`available: false`, return `breakdown: []`, `warnings` explaining the gap, and\nnullable token fields.\n\nDefine context-window usage as model-visible input/context tokens only:\n\n- Include prompt input, system/developer content, tool definitions, MCP tool\n  definitions, skills, memory files, conversation history, tool results, and\n  cache input tokens when response usage is the source.\n- Exclude output tokens and reasoning tokens.\n- Do not report cost/billing totals here. Existing billing APIs own billing.\n\n## Data Model\n\nAdd projection-only, content-free context-window types to\n`lib/crates/fabro-types/src/run_projection.rs`:\n\n- `StageContextWindowProjection`\n- `StageContextWindowBreakdownProjection`\n- category/count/staleness/warning enums, shared with the API through\n  `fabro-api` replacements if the serde shape matches.\n\nExtend `StageProjection` with:\n\n```rust\n#[serde(default, skip_serializing_if = \"Option::is_none\")]\npub context_window: Option<StageContextWindowProjection>,\n```\n\nDo not store raw content. The stored snapshot may contain only:\n\n- provider and model\n- context window size\n- input token total\n- category token counts\n- count method\n- generated timestamp\n- source event sequence\n- warning codes and messages\n\n## Events\n\nAdd a typed event in `lib/crates/fabro-types/src/run_event/agent.rs` and\n`lib/crates/fabro-types/src/run_event/mod.rs`:\n\n```text\nagent.context_window.snapshot\n```\n\nEvent payload should carry the same content-free counts as the projection plus\nthe stage id. The reducer in `lib/crates/fabro-store/src/run_state.rs` should\nreplace the selected stage's `context_window` with the latest snapshot.\n\nThis event is the durable fallback for inactive stages. It should be emitted\nwhen the agent assembles or refreshes the LLM request context, before the\nprovider request is sent. If a provider response later supplies better input\nusage for the same request, emit another snapshot using\n`response_usage_scaled_breakdown`.\n\n## Live Counting Design\n\nThe agent should produce snapshots using this decision order whenever it builds\nan LLM request:\n\n1. Build a content-free local category breakdown from the same inputs used to\n   assemble the `fabro_llm::Request`.\n2. Emit an immediate `agent.context_window.snapshot` with `local_estimate` so a\n   sidebar has data even if provider counting is slow or unavailable.\n3. Attempt provider counting with\n   `Client::count_input_tokens(..., InputTokenCountPreference::PreferProvider)`\n   using the exact in-memory request. This work must not persist or log the raw\n   request.\n4. If provider count succeeds, scale the local category estimates to the\n   provider total and emit a replacement snapshot with\n   `provider_api_scaled_breakdown`.\n5. If provider count falls back or fails, keep the local snapshot and include a\n   warning code on the next emitted snapshot. Count failures must not block the\n   agent's normal LLM request.\n\nThe API endpoint should follow this decision order:\n\n1. Validate the run and stage exist.\n2. If the stage is not an agent stage, return `available: false` with\n   `unavailable_reason: not_agent_stage`.\n3. Return the latest projected `StageContextWindowProjection`.\n4. If no snapshot has ever been observed, return `available: false` with\n   `unavailable_reason: not_observed`.\n\nDo not make the HTTP server own raw LLM requests. The existing server state\ntracks live run control and durable projections, while the exact request exists\ninside the active agent session. Provider counting should therefore happen in\nthe agent/worker process at request-assembly time, and the server endpoint\nshould expose the latest durable snapshot.\n\nIf a future implementation needs user-triggered refreshes, add a separate\nworker request/response control path. Do not tunnel raw request content through\nrun events or store it in `ManagedRun`.\n\nThe live request snapshot needs to be short-lived and content-safe:\n\n- Hold raw `Request` content only inside active agent sessions.\n- Never write that raw request to run events, projection state, logs, or API\n  responses.\n- Cache provider-count results by run id, stage id, provider, model, and request\n  fingerprint or source event seq so each LLM request is counted at most once.\n- Clear the live request handle when the session/stage deactivates.\n\n## Resolved Handoff Decisions\n\n### Decision 1: Category-Aware Estimation Ownership\n\nOptions:\n\n- Agent-only estimator: build all category counts in `fabro-agent`.\n- LLM-only estimator: move the full breakdown model into `fabro-llm`.\n- Hybrid estimator: keep category ownership in `fabro-agent`, but expose small\n  reusable token-estimation helpers from `fabro-llm`.\n\nRecommendation: use the hybrid estimator.\n\nJustification:\n\n- `fabro-agent` has the category knowledge. It sees memory documents, skills,\n  MCP registration, tool registry policy, and the final session history before\n  `Session::build_request` flattens everything into a generic LLM request.\n- `fabro-llm` has the token math and provider-neutral request structures. It\n  already owns local count behavior in `token_count.rs`, so duplicating that\n  estimator in `fabro-agent` would drift.\n- `fabro-llm` should not learn Fabro-specific categories like `skills` or\n  `memory`; that would couple a provider abstraction crate to agent UI\n  semantics.\n\nImplementation guidance:\n\n- Add a `fabro-agent/src/context_window.rs` builder that owns the category\n  taxonomy and content-free snapshot assembly.\n- Expose narrow helpers from `fabro-llm::token_count`, such as tool-definition\n  and message/content-part estimators, instead of making the private estimator\n  logic public wholesale.\n- Keep the existing `Client::count_input_tokens` provider call as the\n  authoritative total when available.\n\n### Decision 2: Provider Count Location\n\nOptions:\n\n- Server-side count: store or reconstruct the exact `fabro_llm::Request` in the\n  HTTP server and call the provider from the endpoint.\n- Synchronous worker query: add a request/response control channel so the HTTP\n  server can ask the active worker for a fresh count on demand.\n- Agent-side count on request assembly: the active session counts the exact\n  request it already has and emits content-free snapshots; the endpoint returns\n  the latest projection.\n\nRecommendation: use agent-side count on request assembly for the first\nimplementation.\n\nJustification:\n\n- It satisfies the requirement that provider counting is attempted by default,\n  because every active agent request can be counted once as it is assembled.\n- It avoids moving raw prompts, memory, tool results, or message history into\n  server-managed state.\n- The existing subprocess control path is one-way JSONL for actions like steer,\n  interrupt, and pair. Adding a synchronous query path just for this read model\n  would be more complex than emitting the durable projection the UI already\n  needs.\n- It works for both subprocess and in-process runs because the agent session is\n  the common place where the exact request exists.\n\nImplementation guidance:\n\n- Emit a local snapshot immediately, then emit a provider-scaled replacement if\n  provider counting succeeds.\n- Do not delay the LLM stream on provider counting unless implementation finds\n  that provider count latency is consistently negligible. A spawned count task\n  with the cloned request is acceptable as long as it is cancelled/ignored when\n  the session closes.\n- Treat provider count errors as snapshot warnings, not stage failures.\n\n### Decision 3: Memory and Skills Attribution\n\nOptions:\n\n- Keep the current flattened system prompt and count all prompt additions as\n  `system_prompt`.\n- Refactor prompt assembly to retain component boundaries, then estimate\n  memory and skills before the final prompt string is concatenated.\n- Add origin metadata to every history message and tool result so activated\n  skill instructions can be attributed even after entering the conversation.\n\nRecommendation: refactor prompt assembly for this first slice; defer full\nhistory-origin metadata.\n\nJustification:\n\n- `assemble_system_prompt` already receives `memory` and `skills` separately,\n  then concatenates them with the core prompt. Returning component metadata from\n  that boundary is a small, local change.\n- This gives useful and accurate first-slice attribution for loaded memory\n  files and the available-skills prompt without changing the persisted\n  conversation format.\n- Activated skill instructions are harder: slash expansion becomes a user turn,\n  and `use_skill` returns a tool result. The current `Message` enum does not\n  preserve source metadata. Adding it is possible, but it is a broader history\n  serialization migration and should not block the endpoint.\n\nImplementation guidance:\n\n- Count the base/core prompt as `system_prompt`.\n- Count memory document text appended by prompt assembly as `memory`.\n- Count the available-skills section and the `use_skill` tool definition as\n  `skills`.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` in this first implementation, with a warning such as\n  `activated_skill_context_counted_as_conversation` when such activations are\n  present.\n\n### Decision 4: Tool vs MCP Tool Attribution\n\nOptions:\n\n- Split MCP tools by name prefix, such as `mcp__`.\n- Add source metadata to `RegisteredTool` / `ToolRegistry`.\n- Recompute MCP membership from `McpConnectionManager` at request time.\n\nRecommendation: add source metadata to `RegisteredTool` / `ToolRegistry`.\n\nJustification:\n\n- The current registry stores only `ToolDefinition` plus executor, so origin is\n  lost after registration.\n- Prefix-based classification matches today's naming convention but is brittle\n  and will misclassify any future native tool that shares the prefix or any MCP\n  naming change.\n- `McpConnectionManager` has source knowledge during registration, but the\n  request builder only sees the final registry and policy-filtered tool\n  definitions.\n\nImplementation guidance:\n\n- Add a small `ToolSource` enum, for example `Native`, `Mcp { server_name }`,\n  and `Skill`.\n- Set `ToolSource::Mcp` in `mcp_integration::make_mcp_tools`.\n- Set `ToolSource::Skill` for `make_use_skill_tool`.\n- Keep existing public `definitions()` behavior unchanged; add a parallel\n  method that returns definitions with source metadata for context-window\n  accounting.\n\n### Decision 5: Unavailable and Error Semantics\n\nOptions:\n\n- Return 404/409 for non-agent stages or stages with no snapshot.\n- Return 200 with `available: false` for known stages where context-window data\n  is not applicable or not observed.\n- Return partial data with warnings for provider-count failures.\n\nRecommendation: return 200 with `available: false` for known-but-unavailable\ndata, and reserve HTTP errors for missing run/stage or malformed requests.\n\nJustification:\n\n- The sidebar needs to render stable empty states without treating normal\n  projection gaps as transport errors.\n- Provider-count support varies by provider and credentials; those are data\n  quality issues, not endpoint availability issues.\n- This matches the broader best-effort contract and avoids UI retry loops when\n  a completed older run simply has no context-window snapshot.\n\nImplementation guidance:\n\n- Use 404 only for missing run or missing stage.\n- Use `available: false` + `unavailable_reason` for `not_agent_stage`,\n  `not_observed`, or `provider_unconfigured`.\n- Use `warnings` for local estimate, provider fallback, ambiguous categories,\n  and activated skill content counted as conversation.\n\nThe previous open questions are resolved by these decisions.\n\n## Implementation Units\n\n### Unit 1: OpenAPI and Generated Clients\n\nFiles:\n\n- `docs/public/api-reference/fabro-api.yaml`\n- `lib/crates/fabro-api/build.rs`\n- `lib/crates/fabro-api/tests/`\n- `lib/packages/fabro-api-client/`\n\nTasks:\n\n- Add the path and schemas under Run Internals.\n- Prefer reusing hand-written Rust projection types through\n  `with_replacement(...)` when serde shape and semantics are identical.\n- Add a `fabro-api` test proving type identity and JSON parity for any new\n  replacement.\n- Regenerate Rust and TypeScript clients.\n\nTests:\n\n- `cargo build -p fabro-api`\n- `cargo nextest run -p fabro-api`\n- `cd lib/packages/fabro-api-client && bun run generate`\n\n### Unit 2: Content-Free Snapshot Builder\n\nFiles:\n\n- `lib/crates/fabro-llm/src/token_count.rs`\n- `lib/crates/fabro-agent/src/context_window.rs` (new)\n- `lib/crates/fabro-agent/src/session.rs`\n- `lib/crates/fabro-agent/src/profiles/mod.rs`\n- `lib/crates/fabro-agent/src/tool_registry.rs`\n- `lib/crates/fabro-agent/src/mcp_integration.rs`\n- `lib/crates/fabro-agent/src/skills.rs`\n- `lib/crates/fabro-agent/src/compaction.rs`\n- `lib/crates/fabro-agent/src/lib.rs`\n\nTasks:\n\n- Build category estimates at the same boundary where `Session::build_request`\n  assembles the `fabro_llm::Request`.\n- Expose narrow reusable token-estimation helpers from `fabro-llm` rather than\n  duplicating the estimator in `fabro-agent`.\n- Refactor prompt assembly enough to retain component boundaries for core\n  system prompt, memory, available skills, and user instructions before the\n  final prompt string is concatenated.\n- Add source metadata to registered tools so the builder can split native tools,\n  MCP tools, and skill-related tools after policy filtering.\n- Classify the system prompt separately from conversation history.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` for the first implementation, with a warning when activated\n  skill context is present.\n- Emit a local snapshot immediately and a provider-scaled replacement snapshot\n  when provider counting succeeds.\n- Keep local estimates deterministic and content-free.\n\nTests:\n\n- system prompt, tools, MCP tools, skills, memory, conversation, and other\n  categories are counted into the expected buckets.\n- category totals equal the local total before provider scaling.\n- provider-scaled totals add up to the provider total.\n- snapshots do not serialize prompt text, memory contents, tool arguments, or\n  message text.\n- opaque/ambiguous inputs produce warnings rather than silent misclassification.\n- provider count failure does not fail the agent turn.\n- each request fingerprint is counted by the provider at most once.\n\n### Unit 3: Event and Projection\n\nFiles:\n\n- `lib/crates/fabro-types/src/run_event/agent.rs`\n- `lib/crates/fabro-types/src/run_event/mod.rs`\n- `lib/crates/fabro-types/src/run_projection.rs`\n- `lib/crates/fabro-store/src/run_state.rs`\n\nTasks:\n\n- Add `agent.context_window.snapshot`.\n- Emit snapshots from the agent session path when requests are assembled and\n  when later response usage improves the count.\n- Project the latest snapshot onto `StageProjection.context_window`.\n- Preserve backwards-compatible deserialization for run projections that do not\n  have the new field.\n\nTests:\n\n- event `type_name()` returns `agent.context_window.snapshot`.\n- reducer updates only the matching stage.\n- later snapshots replace earlier snapshots for the same stage.\n- old projection JSON without `context_window` still deserializes.\n\n### Unit 4: Server Endpoint\n\nFiles:\n\n- `lib/crates/fabro-server/src/server/handler/mod.rs`\n- `lib/crates/fabro-server/src/server/handler/runs.rs` or a new\n  `context_window.rs` handler module\n- `lib/crates/fabro-server/src/server/tests.rs`\n\nTasks:\n\n- Add `GET /runs/{id}/stages/{stageId}/context-window`.\n- Use the same run-scoped authorization pattern as adjacent run internals.\n- Resolve run/stage from the cached projection first for fast 404s and stored\n  fallback.\n- Return the latest projected context-window snapshot for the stage.\n- Return `available: false` for known stages where context-window data is not\n  applicable or has not been observed.\n- Return 404 only for missing run/stage. Provider count failures are represented\n  as snapshot warnings because provider counting happens in the agent.\n\nTests:\n\n- missing run returns 404.\n- missing stage returns 404.\n- non-agent stage returns 200 with `available: false`.\n- projected provider-count success returns `staleness: live` and\n  `count_method: provider_api_scaled_breakdown`.\n- inactive stage returns the latest stored projection snapshot.\n- no observed snapshot returns `available: false` with `not_observed`.\n- projected warning payloads are returned without changing HTTP status.\n\n### Unit 5: Web Query Support\n\nFiles:\n\n- `apps/fabro-web/app/lib/query-keys.ts`\n- `apps/fabro-web/app/lib/queries.ts`\n- `apps/fabro-web/app/lib/run-events.ts`\n- `apps/fabro-web/app/lib/query-keys.test.ts`\n- `apps/fabro-web/app/lib/run-events.test.tsx`\n\nTasks:\n\n- Add `queryKeys.runs.stageContextWindow(id, stageId)`.\n- Add `useRunStageContextWindow(runId, stageId)` using the generated\n  TypeScript client.\n- Invalidate the context-window key for:\n  - `agent.context_window.snapshot`\n  - stage lifecycle events for the same stage\n  - agent activity events that can change the request context\n- Keep the full sidebar UI as follow-up work, but make the hook ready for the\n  agent-node page.\n\nTests:\n\n- query key encodes run id and stage id stably.\n- snapshot event invalidates the context-window key, run events, and stage\n  events.\n- stage lifecycle events invalidate the context-window key for the selected\n  stage.\n- activity events without a stage id do not invalidate unrelated stage context\n  windows.\n\n## Security and Privacy\n\n- Do not persist raw request content to make inactive-stage provider counting\n  possible.\n- Do not log prompt, memory, tool args, or message contents while computing\n  counts.\n- Warning messages should identify count quality, not repeat provider error\n  bodies if those bodies may contain request excerpts.\n- The endpoint should expose counts and category labels only.\n\n## Validation\n\nExpected validation after implementation:\n\n```bash\ncargo build -p fabro-api\ncargo nextest run -p fabro-api -p fabro-agent -p fabro-store -p fabro-server\ncd apps/fabro-web && bun test\ncd apps/fabro-web && bun run typecheck\ncargo +nightly-2026-04-14 fmt --check --all\ngit diff --check\n```\n\n## Remaining Follow-Ups\n\n- Full sidebar visualization.\n- Optional history-origin metadata if we later want activated skill templates\n  to move from `conversation` into `skills`.\n- Optional user-triggered live refresh path if future product needs require\n  provider counting on demand rather than at request assembly time.\n"
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          "graph.goal": "# Context Window Breakdown Endpoint Plan\n\nDate: 2026-05-23\n\n## Context\n\nAgent stage pages already have enough projected data for todos, subagents,\nskills, and MCP servers through `StageProjection` in\n`lib/crates/fabro-types/src/run_projection.rs` and the reducer in\n`lib/crates/fabro-store/src/run_state.rs`. Context-window usage is different:\nFabro emits context-window warnings and compaction events today, but it does not\nstore a category breakdown of the model-visible request context.\n\n`fabro-llm` already exposes the right counting primitive:\n`Client::count_input_tokens(request, InputTokenCountPreference::PreferProvider)`\nin `lib/crates/fabro-llm/src/client.rs`. Provider adapters can call native count\nendpoints for OpenAI, Anthropic, and Gemini, and the client already falls back\nto local estimates for fallback-eligible failures.\n\n## Goal\n\nAdd a best-effort context-window API for agent stages:\n\n```text\nGET /api/v1/runs/{id}/stages/{stageId}/context-window\n```\n\nThe endpoint should return the best context-window usage Fabro can produce with\nno caller-controlled count or accuracy parameters. It may call the configured\nLLM provider by default. If provider counting is not possible, it should degrade\nto a local estimate or the latest stored snapshot instead of making the sidebar\ntreat ordinary count gaps as hard errors.\n\n## Scope\n\nIn scope:\n\n- OpenAPI contract and generated Rust/TypeScript clients.\n- Content-free context-window DTOs in the run projection.\n- A typed agent event for latest context-window snapshots.\n- Server endpoint that combines live provider counting, local estimates, and\n  stored-snapshot fallback.\n- Web query key, hook, and SSE invalidation support so the future sidebar can\n  consume the endpoint.\n\nOut of scope:\n\n- Building the full new left sidebar UI.\n- Persisting raw prompt, memory, tool arguments, or message contents for later\n  token counting.\n- Adding user-visible count-mode or accuracy knobs.\n- Retrofitting exact historical context-window counts for older completed runs.\n\nBefore implementing, read:\n\n- `docs/internal/events-strategy.md`\n- `docs/internal/error-handling-strategy.md`\n- `docs/internal/testing-strategy.md`\n\n## API Contract\n\nAdd the route under the existing Run Internals tag in\n`docs/public/api-reference/fabro-api.yaml`:\n\n```text\nGET /runs/{id}/stages/{stageId}/context-window\n```\n\nProposed response shape:\n\n```json\n{\n  \"stage_id\": \"implement@1\",\n  \"available\": true,\n  \"unavailable_reason\": null,\n  \"provider\": \"openai\",\n  \"model\": \"gpt-5.4\",\n  \"context_window_tokens\": 400000,\n  \"input_tokens\": 123456,\n  \"usage_percent\": 30.86,\n  \"count_method\": \"provider_api_scaled_breakdown\",\n  \"staleness\": \"live\",\n  \"generated_at\": \"2026-05-23T12:34:56Z\",\n  \"event_seq\": 42,\n  \"breakdown\": [\n    {\n      \"category\": \"system_prompt\",\n      \"label\": \"System prompt\",\n      \"tokens\": 30000,\n      \"usage_percent\": 7.5,\n      \"source\": \"scaled_local_estimate\"\n    }\n  ],\n  \"warnings\": []\n}\n```\n\nRequired schemas:\n\n- `StageContextWindow`\n- `StageContextWindowBreakdownItem`\n- `StageContextWindowCategory`\n- `StageContextWindowCountMethod`\n- `StageContextWindowStaleness`\n- `StageContextWindowUnavailableReason`\n- `StageContextWindowWarning`\n\nEnums:\n\n```text\nStageContextWindowCategory:\n  system_prompt\n  tools\n  mcp_tools\n  skills\n  memory\n  conversation\n  other\n\nStageContextWindowCountMethod:\n  provider_api_scaled_breakdown\n  response_usage_scaled_breakdown\n  local_estimate\n\nStageContextWindowStaleness:\n  live\n  stored\n  unavailable\n\nStageContextWindowUnavailableReason:\n  not_agent_stage\n  not_observed\n  provider_unconfigured\n```\n\nUse `available: false` for a real run/stage where Fabro has no context-window\ndata yet. Missing runs and missing stages should still return 404. For\n`available: false`, return `breakdown: []`, `warnings` explaining the gap, and\nnullable token fields.\n\nDefine context-window usage as model-visible input/context tokens only:\n\n- Include prompt input, system/developer content, tool definitions, MCP tool\n  definitions, skills, memory files, conversation history, tool results, and\n  cache input tokens when response usage is the source.\n- Exclude output tokens and reasoning tokens.\n- Do not report cost/billing totals here. Existing billing APIs own billing.\n\n## Data Model\n\nAdd projection-only, content-free context-window types to\n`lib/crates/fabro-types/src/run_projection.rs`:\n\n- `StageContextWindowProjection`\n- `StageContextWindowBreakdownProjection`\n- category/count/staleness/warning enums, shared with the API through\n  `fabro-api` replacements if the serde shape matches.\n\nExtend `StageProjection` with:\n\n```rust\n#[serde(default, skip_serializing_if = \"Option::is_none\")]\npub context_window: Option<StageContextWindowProjection>,\n```\n\nDo not store raw content. The stored snapshot may contain only:\n\n- provider and model\n- context window size\n- input token total\n- category token counts\n- count method\n- generated timestamp\n- source event sequence\n- warning codes and messages\n\n## Events\n\nAdd a typed event in `lib/crates/fabro-types/src/run_event/agent.rs` and\n`lib/crates/fabro-types/src/run_event/mod.rs`:\n\n```text\nagent.context_window.snapshot\n```\n\nEvent payload should carry the same content-free counts as the projection plus\nthe stage id. The reducer in `lib/crates/fabro-store/src/run_state.rs` should\nreplace the selected stage's `context_window` with the latest snapshot.\n\nThis event is the durable fallback for inactive stages. It should be emitted\nwhen the agent assembles or refreshes the LLM request context, before the\nprovider request is sent. If a provider response later supplies better input\nusage for the same request, emit another snapshot using\n`response_usage_scaled_breakdown`.\n\n## Live Counting Design\n\nThe agent should produce snapshots using this decision order whenever it builds\nan LLM request:\n\n1. Build a content-free local category breakdown from the same inputs used to\n   assemble the `fabro_llm::Request`.\n2. Emit an immediate `agent.context_window.snapshot` with `local_estimate` so a\n   sidebar has data even if provider counting is slow or unavailable.\n3. Attempt provider counting with\n   `Client::count_input_tokens(..., InputTokenCountPreference::PreferProvider)`\n   using the exact in-memory request. This work must not persist or log the raw\n   request.\n4. If provider count succeeds, scale the local category estimates to the\n   provider total and emit a replacement snapshot with\n   `provider_api_scaled_breakdown`.\n5. If provider count falls back or fails, keep the local snapshot and include a\n   warning code on the next emitted snapshot. Count failures must not block the\n   agent's normal LLM request.\n\nThe API endpoint should follow this decision order:\n\n1. Validate the run and stage exist.\n2. If the stage is not an agent stage, return `available: false` with\n   `unavailable_reason: not_agent_stage`.\n3. Return the latest projected `StageContextWindowProjection`.\n4. If no snapshot has ever been observed, return `available: false` with\n   `unavailable_reason: not_observed`.\n\nDo not make the HTTP server own raw LLM requests. The existing server state\ntracks live run control and durable projections, while the exact request exists\ninside the active agent session. Provider counting should therefore happen in\nthe agent/worker process at request-assembly time, and the server endpoint\nshould expose the latest durable snapshot.\n\nIf a future implementation needs user-triggered refreshes, add a separate\nworker request/response control path. Do not tunnel raw request content through\nrun events or store it in `ManagedRun`.\n\nThe live request snapshot needs to be short-lived and content-safe:\n\n- Hold raw `Request` content only inside active agent sessions.\n- Never write that raw request to run events, projection state, logs, or API\n  responses.\n- Cache provider-count results by run id, stage id, provider, model, and request\n  fingerprint or source event seq so each LLM request is counted at most once.\n- Clear the live request handle when the session/stage deactivates.\n\n## Resolved Handoff Decisions\n\n### Decision 1: Category-Aware Estimation Ownership\n\nOptions:\n\n- Agent-only estimator: build all category counts in `fabro-agent`.\n- LLM-only estimator: move the full breakdown model into `fabro-llm`.\n- Hybrid estimator: keep category ownership in `fabro-agent`, but expose small\n  reusable token-estimation helpers from `fabro-llm`.\n\nRecommendation: use the hybrid estimator.\n\nJustification:\n\n- `fabro-agent` has the category knowledge. It sees memory documents, skills,\n  MCP registration, tool registry policy, and the final session history before\n  `Session::build_request` flattens everything into a generic LLM request.\n- `fabro-llm` has the token math and provider-neutral request structures. It\n  already owns local count behavior in `token_count.rs`, so duplicating that\n  estimator in `fabro-agent` would drift.\n- `fabro-llm` should not learn Fabro-specific categories like `skills` or\n  `memory`; that would couple a provider abstraction crate to agent UI\n  semantics.\n\nImplementation guidance:\n\n- Add a `fabro-agent/src/context_window.rs` builder that owns the category\n  taxonomy and content-free snapshot assembly.\n- Expose narrow helpers from `fabro-llm::token_count`, such as tool-definition\n  and message/content-part estimators, instead of making the private estimator\n  logic public wholesale.\n- Keep the existing `Client::count_input_tokens` provider call as the\n  authoritative total when available.\n\n### Decision 2: Provider Count Location\n\nOptions:\n\n- Server-side count: store or reconstruct the exact `fabro_llm::Request` in the\n  HTTP server and call the provider from the endpoint.\n- Synchronous worker query: add a request/response control channel so the HTTP\n  server can ask the active worker for a fresh count on demand.\n- Agent-side count on request assembly: the active session counts the exact\n  request it already has and emits content-free snapshots; the endpoint returns\n  the latest projection.\n\nRecommendation: use agent-side count on request assembly for the first\nimplementation.\n\nJustification:\n\n- It satisfies the requirement that provider counting is attempted by default,\n  because every active agent request can be counted once as it is assembled.\n- It avoids moving raw prompts, memory, tool results, or message history into\n  server-managed state.\n- The existing subprocess control path is one-way JSONL for actions like steer,\n  interrupt, and pair. Adding a synchronous query path just for this read model\n  would be more complex than emitting the durable projection the UI already\n  needs.\n- It works for both subprocess and in-process runs because the agent session is\n  the common place where the exact request exists.\n\nImplementation guidance:\n\n- Emit a local snapshot immediately, then emit a provider-scaled replacement if\n  provider counting succeeds.\n- Do not delay the LLM stream on provider counting unless implementation finds\n  that provider count latency is consistently negligible. A spawned count task\n  with the cloned request is acceptable as long as it is cancelled/ignored when\n  the session closes.\n- Treat provider count errors as snapshot warnings, not stage failures.\n\n### Decision 3: Memory and Skills Attribution\n\nOptions:\n\n- Keep the current flattened system prompt and count all prompt additions as\n  `system_prompt`.\n- Refactor prompt assembly to retain component boundaries, then estimate\n  memory and skills before the final prompt string is concatenated.\n- Add origin metadata to every history message and tool result so activated\n  skill instructions can be attributed even after entering the conversation.\n\nRecommendation: refactor prompt assembly for this first slice; defer full\nhistory-origin metadata.\n\nJustification:\n\n- `assemble_system_prompt` already receives `memory` and `skills` separately,\n  then concatenates them with the core prompt. Returning component metadata from\n  that boundary is a small, local change.\n- This gives useful and accurate first-slice attribution for loaded memory\n  files and the available-skills prompt without changing the persisted\n  conversation format.\n- Activated skill instructions are harder: slash expansion becomes a user turn,\n  and `use_skill` returns a tool result. The current `Message` enum does not\n  preserve source metadata. Adding it is possible, but it is a broader history\n  serialization migration and should not block the endpoint.\n\nImplementation guidance:\n\n- Count the base/core prompt as `system_prompt`.\n- Count memory document text appended by prompt assembly as `memory`.\n- Count the available-skills section and the `use_skill` tool definition as\n  `skills`.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` in this first implementation, with a warning such as\n  `activated_skill_context_counted_as_conversation` when such activations are\n  present.\n\n### Decision 4: Tool vs MCP Tool Attribution\n\nOptions:\n\n- Split MCP tools by name prefix, such as `mcp__`.\n- Add source metadata to `RegisteredTool` / `ToolRegistry`.\n- Recompute MCP membership from `McpConnectionManager` at request time.\n\nRecommendation: add source metadata to `RegisteredTool` / `ToolRegistry`.\n\nJustification:\n\n- The current registry stores only `ToolDefinition` plus executor, so origin is\n  lost after registration.\n- Prefix-based classification matches today's naming convention but is brittle\n  and will misclassify any future native tool that shares the prefix or any MCP\n  naming change.\n- `McpConnectionManager` has source knowledge during registration, but the\n  request builder only sees the final registry and policy-filtered tool\n  definitions.\n\nImplementation guidance:\n\n- Add a small `ToolSource` enum, for example `Native`, `Mcp { server_name }`,\n  and `Skill`.\n- Set `ToolSource::Mcp` in `mcp_integration::make_mcp_tools`.\n- Set `ToolSource::Skill` for `make_use_skill_tool`.\n- Keep existing public `definitions()` behavior unchanged; add a parallel\n  method that returns definitions with source metadata for context-window\n  accounting.\n\n### Decision 5: Unavailable and Error Semantics\n\nOptions:\n\n- Return 404/409 for non-agent stages or stages with no snapshot.\n- Return 200 with `available: false` for known stages where context-window data\n  is not applicable or not observed.\n- Return partial data with warnings for provider-count failures.\n\nRecommendation: return 200 with `available: false` for known-but-unavailable\ndata, and reserve HTTP errors for missing run/stage or malformed requests.\n\nJustification:\n\n- The sidebar needs to render stable empty states without treating normal\n  projection gaps as transport errors.\n- Provider-count support varies by provider and credentials; those are data\n  quality issues, not endpoint availability issues.\n- This matches the broader best-effort contract and avoids UI retry loops when\n  a completed older run simply has no context-window snapshot.\n\nImplementation guidance:\n\n- Use 404 only for missing run or missing stage.\n- Use `available: false` + `unavailable_reason` for `not_agent_stage`,\n  `not_observed`, or `provider_unconfigured`.\n- Use `warnings` for local estimate, provider fallback, ambiguous categories,\n  and activated skill content counted as conversation.\n\nThe previous open questions are resolved by these decisions.\n\n## Implementation Units\n\n### Unit 1: OpenAPI and Generated Clients\n\nFiles:\n\n- `docs/public/api-reference/fabro-api.yaml`\n- `lib/crates/fabro-api/build.rs`\n- `lib/crates/fabro-api/tests/`\n- `lib/packages/fabro-api-client/`\n\nTasks:\n\n- Add the path and schemas under Run Internals.\n- Prefer reusing hand-written Rust projection types through\n  `with_replacement(...)` when serde shape and semantics are identical.\n- Add a `fabro-api` test proving type identity and JSON parity for any new\n  replacement.\n- Regenerate Rust and TypeScript clients.\n\nTests:\n\n- `cargo build -p fabro-api`\n- `cargo nextest run -p fabro-api`\n- `cd lib/packages/fabro-api-client && bun run generate`\n\n### Unit 2: Content-Free Snapshot Builder\n\nFiles:\n\n- `lib/crates/fabro-llm/src/token_count.rs`\n- `lib/crates/fabro-agent/src/context_window.rs` (new)\n- `lib/crates/fabro-agent/src/session.rs`\n- `lib/crates/fabro-agent/src/profiles/mod.rs`\n- `lib/crates/fabro-agent/src/tool_registry.rs`\n- `lib/crates/fabro-agent/src/mcp_integration.rs`\n- `lib/crates/fabro-agent/src/skills.rs`\n- `lib/crates/fabro-agent/src/compaction.rs`\n- `lib/crates/fabro-agent/src/lib.rs`\n\nTasks:\n\n- Build category estimates at the same boundary where `Session::build_request`\n  assembles the `fabro_llm::Request`.\n- Expose narrow reusable token-estimation helpers from `fabro-llm` rather than\n  duplicating the estimator in `fabro-agent`.\n- Refactor prompt assembly enough to retain component boundaries for core\n  system prompt, memory, available skills, and user instructions before the\n  final prompt string is concatenated.\n- Add source metadata to registered tools so the builder can split native tools,\n  MCP tools, and skill-related tools after policy filtering.\n- Classify the system prompt separately from conversation history.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` for the first implementation, with a warning when activated\n  skill context is present.\n- Emit a local snapshot immediately and a provider-scaled replacement snapshot\n  when provider counting succeeds.\n- Keep local estimates deterministic and content-free.\n\nTests:\n\n- system prompt, tools, MCP tools, skills, memory, conversation, and other\n  categories are counted into the expected buckets.\n- category totals equal the local total before provider scaling.\n- provider-scaled totals add up to the provider total.\n- snapshots do not serialize prompt text, memory contents, tool arguments, or\n  message text.\n- opaque/ambiguous inputs produce warnings rather than silent misclassification.\n- provider count failure does not fail the agent turn.\n- each request fingerprint is counted by the provider at most once.\n\n### Unit 3: Event and Projection\n\nFiles:\n\n- `lib/crates/fabro-types/src/run_event/agent.rs`\n- `lib/crates/fabro-types/src/run_event/mod.rs`\n- `lib/crates/fabro-types/src/run_projection.rs`\n- `lib/crates/fabro-store/src/run_state.rs`\n\nTasks:\n\n- Add `agent.context_window.snapshot`.\n- Emit snapshots from the agent session path when requests are assembled and\n  when later response usage improves the count.\n- Project the latest snapshot onto `StageProjection.context_window`.\n- Preserve backwards-compatible deserialization for run projections that do not\n  have the new field.\n\nTests:\n\n- event `type_name()` returns `agent.context_window.snapshot`.\n- reducer updates only the matching stage.\n- later snapshots replace earlier snapshots for the same stage.\n- old projection JSON without `context_window` still deserializes.\n\n### Unit 4: Server Endpoint\n\nFiles:\n\n- `lib/crates/fabro-server/src/server/handler/mod.rs`\n- `lib/crates/fabro-server/src/server/handler/runs.rs` or a new\n  `context_window.rs` handler module\n- `lib/crates/fabro-server/src/server/tests.rs`\n\nTasks:\n\n- Add `GET /runs/{id}/stages/{stageId}/context-window`.\n- Use the same run-scoped authorization pattern as adjacent run internals.\n- Resolve run/stage from the cached projection first for fast 404s and stored\n  fallback.\n- Return the latest projected context-window snapshot for the stage.\n- Return `available: false` for known stages where context-window data is not\n  applicable or has not been observed.\n- Return 404 only for missing run/stage. Provider count failures are represented\n  as snapshot warnings because provider counting happens in the agent.\n\nTests:\n\n- missing run returns 404.\n- missing stage returns 404.\n- non-agent stage returns 200 with `available: false`.\n- projected provider-count success returns `staleness: live` and\n  `count_method: provider_api_scaled_breakdown`.\n- inactive stage returns the latest stored projection snapshot.\n- no observed snapshot returns `available: false` with `not_observed`.\n- projected warning payloads are returned without changing HTTP status.\n\n### Unit 5: Web Query Support\n\nFiles:\n\n- `apps/fabro-web/app/lib/query-keys.ts`\n- `apps/fabro-web/app/lib/queries.ts`\n- `apps/fabro-web/app/lib/run-events.ts`\n- `apps/fabro-web/app/lib/query-keys.test.ts`\n- `apps/fabro-web/app/lib/run-events.test.tsx`\n\nTasks:\n\n- Add `queryKeys.runs.stageContextWindow(id, stageId)`.\n- Add `useRunStageContextWindow(runId, stageId)` using the generated\n  TypeScript client.\n- Invalidate the context-window key for:\n  - `agent.context_window.snapshot`\n  - stage lifecycle events for the same stage\n  - agent activity events that can change the request context\n- Keep the full sidebar UI as follow-up work, but make the hook ready for the\n  agent-node page.\n\nTests:\n\n- query key encodes run id and stage id stably.\n- snapshot event invalidates the context-window key, run events, and stage\n  events.\n- stage lifecycle events invalidate the context-window key for the selected\n  stage.\n- activity events without a stage id do not invalidate unrelated stage context\n  windows.\n\n## Security and Privacy\n\n- Do not persist raw request content to make inactive-stage provider counting\n  possible.\n- Do not log prompt, memory, tool args, or message contents while computing\n  counts.\n- Warning messages should identify count quality, not repeat provider error\n  bodies if those bodies may contain request excerpts.\n- The endpoint should expose counts and category labels only.\n\n## Validation\n\nExpected validation after implementation:\n\n```bash\ncargo build -p fabro-api\ncargo nextest run -p fabro-api -p fabro-agent -p fabro-store -p fabro-server\ncd apps/fabro-web && bun test\ncd apps/fabro-web && bun run typecheck\ncargo +nightly-2026-04-14 fmt --check --all\ngit diff --check\n```\n\n## Remaining Follow-Ups\n\n- Full sidebar visualization.\n- Optional history-origin metadata if we later want activated skill templates\n  to move from `conversation` into `skills`.\n- Optional user-triggered live refresh path if future product needs require\n  provider counting on demand rather than at request assembly time.\n",
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          "graph.goal": "# Context Window Breakdown Endpoint Plan\n\nDate: 2026-05-23\n\n## Context\n\nAgent stage pages already have enough projected data for todos, subagents,\nskills, and MCP servers through `StageProjection` in\n`lib/crates/fabro-types/src/run_projection.rs` and the reducer in\n`lib/crates/fabro-store/src/run_state.rs`. Context-window usage is different:\nFabro emits context-window warnings and compaction events today, but it does not\nstore a category breakdown of the model-visible request context.\n\n`fabro-llm` already exposes the right counting primitive:\n`Client::count_input_tokens(request, InputTokenCountPreference::PreferProvider)`\nin `lib/crates/fabro-llm/src/client.rs`. Provider adapters can call native count\nendpoints for OpenAI, Anthropic, and Gemini, and the client already falls back\nto local estimates for fallback-eligible failures.\n\n## Goal\n\nAdd a best-effort context-window API for agent stages:\n\n```text\nGET /api/v1/runs/{id}/stages/{stageId}/context-window\n```\n\nThe endpoint should return the best context-window usage Fabro can produce with\nno caller-controlled count or accuracy parameters. It may call the configured\nLLM provider by default. If provider counting is not possible, it should degrade\nto a local estimate or the latest stored snapshot instead of making the sidebar\ntreat ordinary count gaps as hard errors.\n\n## Scope\n\nIn scope:\n\n- OpenAPI contract and generated Rust/TypeScript clients.\n- Content-free context-window DTOs in the run projection.\n- A typed agent event for latest context-window snapshots.\n- Server endpoint that combines live provider counting, local estimates, and\n  stored-snapshot fallback.\n- Web query key, hook, and SSE invalidation support so the future sidebar can\n  consume the endpoint.\n\nOut of scope:\n\n- Building the full new left sidebar UI.\n- Persisting raw prompt, memory, tool arguments, or message contents for later\n  token counting.\n- Adding user-visible count-mode or accuracy knobs.\n- Retrofitting exact historical context-window counts for older completed runs.\n\nBefore implementing, read:\n\n- `docs/internal/events-strategy.md`\n- `docs/internal/error-handling-strategy.md`\n- `docs/internal/testing-strategy.md`\n\n## API Contract\n\nAdd the route under the existing Run Internals tag in\n`docs/public/api-reference/fabro-api.yaml`:\n\n```text\nGET /runs/{id}/stages/{stageId}/context-window\n```\n\nProposed response shape:\n\n```json\n{\n  \"stage_id\": \"implement@1\",\n  \"available\": true,\n  \"unavailable_reason\": null,\n  \"provider\": \"openai\",\n  \"model\": \"gpt-5.4\",\n  \"context_window_tokens\": 400000,\n  \"input_tokens\": 123456,\n  \"usage_percent\": 30.86,\n  \"count_method\": \"provider_api_scaled_breakdown\",\n  \"staleness\": \"live\",\n  \"generated_at\": \"2026-05-23T12:34:56Z\",\n  \"event_seq\": 42,\n  \"breakdown\": [\n    {\n      \"category\": \"system_prompt\",\n      \"label\": \"System prompt\",\n      \"tokens\": 30000,\n      \"usage_percent\": 7.5,\n      \"source\": \"scaled_local_estimate\"\n    }\n  ],\n  \"warnings\": []\n}\n```\n\nRequired schemas:\n\n- `StageContextWindow`\n- `StageContextWindowBreakdownItem`\n- `StageContextWindowCategory`\n- `StageContextWindowCountMethod`\n- `StageContextWindowStaleness`\n- `StageContextWindowUnavailableReason`\n- `StageContextWindowWarning`\n\nEnums:\n\n```text\nStageContextWindowCategory:\n  system_prompt\n  tools\n  mcp_tools\n  skills\n  memory\n  conversation\n  other\n\nStageContextWindowCountMethod:\n  provider_api_scaled_breakdown\n  response_usage_scaled_breakdown\n  local_estimate\n\nStageContextWindowStaleness:\n  live\n  stored\n  unavailable\n\nStageContextWindowUnavailableReason:\n  not_agent_stage\n  not_observed\n  provider_unconfigured\n```\n\nUse `available: false` for a real run/stage where Fabro has no context-window\ndata yet. Missing runs and missing stages should still return 404. For\n`available: false`, return `breakdown: []`, `warnings` explaining the gap, and\nnullable token fields.\n\nDefine context-window usage as model-visible input/context tokens only:\n\n- Include prompt input, system/developer content, tool definitions, MCP tool\n  definitions, skills, memory files, conversation history, tool results, and\n  cache input tokens when response usage is the source.\n- Exclude output tokens and reasoning tokens.\n- Do not report cost/billing totals here. Existing billing APIs own billing.\n\n## Data Model\n\nAdd projection-only, content-free context-window types to\n`lib/crates/fabro-types/src/run_projection.rs`:\n\n- `StageContextWindowProjection`\n- `StageContextWindowBreakdownProjection`\n- category/count/staleness/warning enums, shared with the API through\n  `fabro-api` replacements if the serde shape matches.\n\nExtend `StageProjection` with:\n\n```rust\n#[serde(default, skip_serializing_if = \"Option::is_none\")]\npub context_window: Option<StageContextWindowProjection>,\n```\n\nDo not store raw content. The stored snapshot may contain only:\n\n- provider and model\n- context window size\n- input token total\n- category token counts\n- count method\n- generated timestamp\n- source event sequence\n- warning codes and messages\n\n## Events\n\nAdd a typed event in `lib/crates/fabro-types/src/run_event/agent.rs` and\n`lib/crates/fabro-types/src/run_event/mod.rs`:\n\n```text\nagent.context_window.snapshot\n```\n\nEvent payload should carry the same content-free counts as the projection plus\nthe stage id. The reducer in `lib/crates/fabro-store/src/run_state.rs` should\nreplace the selected stage's `context_window` with the latest snapshot.\n\nThis event is the durable fallback for inactive stages. It should be emitted\nwhen the agent assembles or refreshes the LLM request context, before the\nprovider request is sent. If a provider response later supplies better input\nusage for the same request, emit another snapshot using\n`response_usage_scaled_breakdown`.\n\n## Live Counting Design\n\nThe agent should produce snapshots using this decision order whenever it builds\nan LLM request:\n\n1. Build a content-free local category breakdown from the same inputs used to\n   assemble the `fabro_llm::Request`.\n2. Emit an immediate `agent.context_window.snapshot` with `local_estimate` so a\n   sidebar has data even if provider counting is slow or unavailable.\n3. Attempt provider counting with\n   `Client::count_input_tokens(..., InputTokenCountPreference::PreferProvider)`\n   using the exact in-memory request. This work must not persist or log the raw\n   request.\n4. If provider count succeeds, scale the local category estimates to the\n   provider total and emit a replacement snapshot with\n   `provider_api_scaled_breakdown`.\n5. If provider count falls back or fails, keep the local snapshot and include a\n   warning code on the next emitted snapshot. Count failures must not block the\n   agent's normal LLM request.\n\nThe API endpoint should follow this decision order:\n\n1. Validate the run and stage exist.\n2. If the stage is not an agent stage, return `available: false` with\n   `unavailable_reason: not_agent_stage`.\n3. Return the latest projected `StageContextWindowProjection`.\n4. If no snapshot has ever been observed, return `available: false` with\n   `unavailable_reason: not_observed`.\n\nDo not make the HTTP server own raw LLM requests. The existing server state\ntracks live run control and durable projections, while the exact request exists\ninside the active agent session. Provider counting should therefore happen in\nthe agent/worker process at request-assembly time, and the server endpoint\nshould expose the latest durable snapshot.\n\nIf a future implementation needs user-triggered refreshes, add a separate\nworker request/response control path. Do not tunnel raw request content through\nrun events or store it in `ManagedRun`.\n\nThe live request snapshot needs to be short-lived and content-safe:\n\n- Hold raw `Request` content only inside active agent sessions.\n- Never write that raw request to run events, projection state, logs, or API\n  responses.\n- Cache provider-count results by run id, stage id, provider, model, and request\n  fingerprint or source event seq so each LLM request is counted at most once.\n- Clear the live request handle when the session/stage deactivates.\n\n## Resolved Handoff Decisions\n\n### Decision 1: Category-Aware Estimation Ownership\n\nOptions:\n\n- Agent-only estimator: build all category counts in `fabro-agent`.\n- LLM-only estimator: move the full breakdown model into `fabro-llm`.\n- Hybrid estimator: keep category ownership in `fabro-agent`, but expose small\n  reusable token-estimation helpers from `fabro-llm`.\n\nRecommendation: use the hybrid estimator.\n\nJustification:\n\n- `fabro-agent` has the category knowledge. It sees memory documents, skills,\n  MCP registration, tool registry policy, and the final session history before\n  `Session::build_request` flattens everything into a generic LLM request.\n- `fabro-llm` has the token math and provider-neutral request structures. It\n  already owns local count behavior in `token_count.rs`, so duplicating that\n  estimator in `fabro-agent` would drift.\n- `fabro-llm` should not learn Fabro-specific categories like `skills` or\n  `memory`; that would couple a provider abstraction crate to agent UI\n  semantics.\n\nImplementation guidance:\n\n- Add a `fabro-agent/src/context_window.rs` builder that owns the category\n  taxonomy and content-free snapshot assembly.\n- Expose narrow helpers from `fabro-llm::token_count`, such as tool-definition\n  and message/content-part estimators, instead of making the private estimator\n  logic public wholesale.\n- Keep the existing `Client::count_input_tokens` provider call as the\n  authoritative total when available.\n\n### Decision 2: Provider Count Location\n\nOptions:\n\n- Server-side count: store or reconstruct the exact `fabro_llm::Request` in the\n  HTTP server and call the provider from the endpoint.\n- Synchronous worker query: add a request/response control channel so the HTTP\n  server can ask the active worker for a fresh count on demand.\n- Agent-side count on request assembly: the active session counts the exact\n  request it already has and emits content-free snapshots; the endpoint returns\n  the latest projection.\n\nRecommendation: use agent-side count on request assembly for the first\nimplementation.\n\nJustification:\n\n- It satisfies the requirement that provider counting is attempted by default,\n  because every active agent request can be counted once as it is assembled.\n- It avoids moving raw prompts, memory, tool results, or message history into\n  server-managed state.\n- The existing subprocess control path is one-way JSONL for actions like steer,\n  interrupt, and pair. Adding a synchronous query path just for this read model\n  would be more complex than emitting the durable projection the UI already\n  needs.\n- It works for both subprocess and in-process runs because the agent session is\n  the common place where the exact request exists.\n\nImplementation guidance:\n\n- Emit a local snapshot immediately, then emit a provider-scaled replacement if\n  provider counting succeeds.\n- Do not delay the LLM stream on provider counting unless implementation finds\n  that provider count latency is consistently negligible. A spawned count task\n  with the cloned request is acceptable as long as it is cancelled/ignored when\n  the session closes.\n- Treat provider count errors as snapshot warnings, not stage failures.\n\n### Decision 3: Memory and Skills Attribution\n\nOptions:\n\n- Keep the current flattened system prompt and count all prompt additions as\n  `system_prompt`.\n- Refactor prompt assembly to retain component boundaries, then estimate\n  memory and skills before the final prompt string is concatenated.\n- Add origin metadata to every history message and tool result so activated\n  skill instructions can be attributed even after entering the conversation.\n\nRecommendation: refactor prompt assembly for this first slice; defer full\nhistory-origin metadata.\n\nJustification:\n\n- `assemble_system_prompt` already receives `memory` and `skills` separately,\n  then concatenates them with the core prompt. Returning component metadata from\n  that boundary is a small, local change.\n- This gives useful and accurate first-slice attribution for loaded memory\n  files and the available-skills prompt without changing the persisted\n  conversation format.\n- Activated skill instructions are harder: slash expansion becomes a user turn,\n  and `use_skill` returns a tool result. The current `Message` enum does not\n  preserve source metadata. Adding it is possible, but it is a broader history\n  serialization migration and should not block the endpoint.\n\nImplementation guidance:\n\n- Count the base/core prompt as `system_prompt`.\n- Count memory document text appended by prompt assembly as `memory`.\n- Count the available-skills section and the `use_skill` tool definition as\n  `skills`.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` in this first implementation, with a warning such as\n  `activated_skill_context_counted_as_conversation` when such activations are\n  present.\n\n### Decision 4: Tool vs MCP Tool Attribution\n\nOptions:\n\n- Split MCP tools by name prefix, such as `mcp__`.\n- Add source metadata to `RegisteredTool` / `ToolRegistry`.\n- Recompute MCP membership from `McpConnectionManager` at request time.\n\nRecommendation: add source metadata to `RegisteredTool` / `ToolRegistry`.\n\nJustification:\n\n- The current registry stores only `ToolDefinition` plus executor, so origin is\n  lost after registration.\n- Prefix-based classification matches today's naming convention but is brittle\n  and will misclassify any future native tool that shares the prefix or any MCP\n  naming change.\n- `McpConnectionManager` has source knowledge during registration, but the\n  request builder only sees the final registry and policy-filtered tool\n  definitions.\n\nImplementation guidance:\n\n- Add a small `ToolSource` enum, for example `Native`, `Mcp { server_name }`,\n  and `Skill`.\n- Set `ToolSource::Mcp` in `mcp_integration::make_mcp_tools`.\n- Set `ToolSource::Skill` for `make_use_skill_tool`.\n- Keep existing public `definitions()` behavior unchanged; add a parallel\n  method that returns definitions with source metadata for context-window\n  accounting.\n\n### Decision 5: Unavailable and Error Semantics\n\nOptions:\n\n- Return 404/409 for non-agent stages or stages with no snapshot.\n- Return 200 with `available: false` for known stages where context-window data\n  is not applicable or not observed.\n- Return partial data with warnings for provider-count failures.\n\nRecommendation: return 200 with `available: false` for known-but-unavailable\ndata, and reserve HTTP errors for missing run/stage or malformed requests.\n\nJustification:\n\n- The sidebar needs to render stable empty states without treating normal\n  projection gaps as transport errors.\n- Provider-count support varies by provider and credentials; those are data\n  quality issues, not endpoint availability issues.\n- This matches the broader best-effort contract and avoids UI retry loops when\n  a completed older run simply has no context-window snapshot.\n\nImplementation guidance:\n\n- Use 404 only for missing run or missing stage.\n- Use `available: false` + `unavailable_reason` for `not_agent_stage`,\n  `not_observed`, or `provider_unconfigured`.\n- Use `warnings` for local estimate, provider fallback, ambiguous categories,\n  and activated skill content counted as conversation.\n\nThe previous open questions are resolved by these decisions.\n\n## Implementation Units\n\n### Unit 1: OpenAPI and Generated Clients\n\nFiles:\n\n- `docs/public/api-reference/fabro-api.yaml`\n- `lib/crates/fabro-api/build.rs`\n- `lib/crates/fabro-api/tests/`\n- `lib/packages/fabro-api-client/`\n\nTasks:\n\n- Add the path and schemas under Run Internals.\n- Prefer reusing hand-written Rust projection types through\n  `with_replacement(...)` when serde shape and semantics are identical.\n- Add a `fabro-api` test proving type identity and JSON parity for any new\n  replacement.\n- Regenerate Rust and TypeScript clients.\n\nTests:\n\n- `cargo build -p fabro-api`\n- `cargo nextest run -p fabro-api`\n- `cd lib/packages/fabro-api-client && bun run generate`\n\n### Unit 2: Content-Free Snapshot Builder\n\nFiles:\n\n- `lib/crates/fabro-llm/src/token_count.rs`\n- `lib/crates/fabro-agent/src/context_window.rs` (new)\n- `lib/crates/fabro-agent/src/session.rs`\n- `lib/crates/fabro-agent/src/profiles/mod.rs`\n- `lib/crates/fabro-agent/src/tool_registry.rs`\n- `lib/crates/fabro-agent/src/mcp_integration.rs`\n- `lib/crates/fabro-agent/src/skills.rs`\n- `lib/crates/fabro-agent/src/compaction.rs`\n- `lib/crates/fabro-agent/src/lib.rs`\n\nTasks:\n\n- Build category estimates at the same boundary where `Session::build_request`\n  assembles the `fabro_llm::Request`.\n- Expose narrow reusable token-estimation helpers from `fabro-llm` rather than\n  duplicating the estimator in `fabro-agent`.\n- Refactor prompt assembly enough to retain component boundaries for core\n  system prompt, memory, available skills, and user instructions before the\n  final prompt string is concatenated.\n- Add source metadata to registered tools so the builder can split native tools,\n  MCP tools, and skill-related tools after policy filtering.\n- Classify the system prompt separately from conversation history.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` for the first implementation, with a warning when activated\n  skill context is present.\n- Emit a local snapshot immediately and a provider-scaled replacement snapshot\n  when provider counting succeeds.\n- Keep local estimates deterministic and content-free.\n\nTests:\n\n- system prompt, tools, MCP tools, skills, memory, conversation, and other\n  categories are counted into the expected buckets.\n- category totals equal the local total before provider scaling.\n- provider-scaled totals add up to the provider total.\n- snapshots do not serialize prompt text, memory contents, tool arguments, or\n  message text.\n- opaque/ambiguous inputs produce warnings rather than silent misclassification.\n- provider count failure does not fail the agent turn.\n- each request fingerprint is counted by the provider at most once.\n\n### Unit 3: Event and Projection\n\nFiles:\n\n- `lib/crates/fabro-types/src/run_event/agent.rs`\n- `lib/crates/fabro-types/src/run_event/mod.rs`\n- `lib/crates/fabro-types/src/run_projection.rs`\n- `lib/crates/fabro-store/src/run_state.rs`\n\nTasks:\n\n- Add `agent.context_window.snapshot`.\n- Emit snapshots from the agent session path when requests are assembled and\n  when later response usage improves the count.\n- Project the latest snapshot onto `StageProjection.context_window`.\n- Preserve backwards-compatible deserialization for run projections that do not\n  have the new field.\n\nTests:\n\n- event `type_name()` returns `agent.context_window.snapshot`.\n- reducer updates only the matching stage.\n- later snapshots replace earlier snapshots for the same stage.\n- old projection JSON without `context_window` still deserializes.\n\n### Unit 4: Server Endpoint\n\nFiles:\n\n- `lib/crates/fabro-server/src/server/handler/mod.rs`\n- `lib/crates/fabro-server/src/server/handler/runs.rs` or a new\n  `context_window.rs` handler module\n- `lib/crates/fabro-server/src/server/tests.rs`\n\nTasks:\n\n- Add `GET /runs/{id}/stages/{stageId}/context-window`.\n- Use the same run-scoped authorization pattern as adjacent run internals.\n- Resolve run/stage from the cached projection first for fast 404s and stored\n  fallback.\n- Return the latest projected context-window snapshot for the stage.\n- Return `available: false` for known stages where context-window data is not\n  applicable or has not been observed.\n- Return 404 only for missing run/stage. Provider count failures are represented\n  as snapshot warnings because provider counting happens in the agent.\n\nTests:\n\n- missing run returns 404.\n- missing stage returns 404.\n- non-agent stage returns 200 with `available: false`.\n- projected provider-count success returns `staleness: live` and\n  `count_method: provider_api_scaled_breakdown`.\n- inactive stage returns the latest stored projection snapshot.\n- no observed snapshot returns `available: false` with `not_observed`.\n- projected warning payloads are returned without changing HTTP status.\n\n### Unit 5: Web Query Support\n\nFiles:\n\n- `apps/fabro-web/app/lib/query-keys.ts`\n- `apps/fabro-web/app/lib/queries.ts`\n- `apps/fabro-web/app/lib/run-events.ts`\n- `apps/fabro-web/app/lib/query-keys.test.ts`\n- `apps/fabro-web/app/lib/run-events.test.tsx`\n\nTasks:\n\n- Add `queryKeys.runs.stageContextWindow(id, stageId)`.\n- Add `useRunStageContextWindow(runId, stageId)` using the generated\n  TypeScript client.\n- Invalidate the context-window key for:\n  - `agent.context_window.snapshot`\n  - stage lifecycle events for the same stage\n  - agent activity events that can change the request context\n- Keep the full sidebar UI as follow-up work, but make the hook ready for the\n  agent-node page.\n\nTests:\n\n- query key encodes run id and stage id stably.\n- snapshot event invalidates the context-window key, run events, and stage\n  events.\n- stage lifecycle events invalidate the context-window key for the selected\n  stage.\n- activity events without a stage id do not invalidate unrelated stage context\n  windows.\n\n## Security and Privacy\n\n- Do not persist raw request content to make inactive-stage provider counting\n  possible.\n- Do not log prompt, memory, tool args, or message contents while computing\n  counts.\n- Warning messages should identify count quality, not repeat provider error\n  bodies if those bodies may contain request excerpts.\n- The endpoint should expose counts and category labels only.\n\n## Validation\n\nExpected validation after implementation:\n\n```bash\ncargo build -p fabro-api\ncargo nextest run -p fabro-api -p fabro-agent -p fabro-store -p fabro-server\ncd apps/fabro-web && bun test\ncd apps/fabro-web && bun run typecheck\ncargo +nightly-2026-04-14 fmt --check --all\ngit diff --check\n```\n\n## Remaining Follow-Ups\n\n- Full sidebar visualization.\n- Optional history-origin metadata if we later want activated skill templates\n  to move from `conversation` into `skills`.\n- Optional user-triggered live refresh path if future product needs require\n  provider counting on demand rather than at request assembly time.\n",
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          "graph.goal": "# Context Window Breakdown Endpoint Plan\n\nDate: 2026-05-23\n\n## Context\n\nAgent stage pages already have enough projected data for todos, subagents,\nskills, and MCP servers through `StageProjection` in\n`lib/crates/fabro-types/src/run_projection.rs` and the reducer in\n`lib/crates/fabro-store/src/run_state.rs`. Context-window usage is different:\nFabro emits context-window warnings and compaction events today, but it does not\nstore a category breakdown of the model-visible request context.\n\n`fabro-llm` already exposes the right counting primitive:\n`Client::count_input_tokens(request, InputTokenCountPreference::PreferProvider)`\nin `lib/crates/fabro-llm/src/client.rs`. Provider adapters can call native count\nendpoints for OpenAI, Anthropic, and Gemini, and the client already falls back\nto local estimates for fallback-eligible failures.\n\n## Goal\n\nAdd a best-effort context-window API for agent stages:\n\n```text\nGET /api/v1/runs/{id}/stages/{stageId}/context-window\n```\n\nThe endpoint should return the best context-window usage Fabro can produce with\nno caller-controlled count or accuracy parameters. It may call the configured\nLLM provider by default. If provider counting is not possible, it should degrade\nto a local estimate or the latest stored snapshot instead of making the sidebar\ntreat ordinary count gaps as hard errors.\n\n## Scope\n\nIn scope:\n\n- OpenAPI contract and generated Rust/TypeScript clients.\n- Content-free context-window DTOs in the run projection.\n- A typed agent event for latest context-window snapshots.\n- Server endpoint that combines live provider counting, local estimates, and\n  stored-snapshot fallback.\n- Web query key, hook, and SSE invalidation support so the future sidebar can\n  consume the endpoint.\n\nOut of scope:\n\n- Building the full new left sidebar UI.\n- Persisting raw prompt, memory, tool arguments, or message contents for later\n  token counting.\n- Adding user-visible count-mode or accuracy knobs.\n- Retrofitting exact historical context-window counts for older completed runs.\n\nBefore implementing, read:\n\n- `docs/internal/events-strategy.md`\n- `docs/internal/error-handling-strategy.md`\n- `docs/internal/testing-strategy.md`\n\n## API Contract\n\nAdd the route under the existing Run Internals tag in\n`docs/public/api-reference/fabro-api.yaml`:\n\n```text\nGET /runs/{id}/stages/{stageId}/context-window\n```\n\nProposed response shape:\n\n```json\n{\n  \"stage_id\": \"implement@1\",\n  \"available\": true,\n  \"unavailable_reason\": null,\n  \"provider\": \"openai\",\n  \"model\": \"gpt-5.4\",\n  \"context_window_tokens\": 400000,\n  \"input_tokens\": 123456,\n  \"usage_percent\": 30.86,\n  \"count_method\": \"provider_api_scaled_breakdown\",\n  \"staleness\": \"live\",\n  \"generated_at\": \"2026-05-23T12:34:56Z\",\n  \"event_seq\": 42,\n  \"breakdown\": [\n    {\n      \"category\": \"system_prompt\",\n      \"label\": \"System prompt\",\n      \"tokens\": 30000,\n      \"usage_percent\": 7.5,\n      \"source\": \"scaled_local_estimate\"\n    }\n  ],\n  \"warnings\": []\n}\n```\n\nRequired schemas:\n\n- `StageContextWindow`\n- `StageContextWindowBreakdownItem`\n- `StageContextWindowCategory`\n- `StageContextWindowCountMethod`\n- `StageContextWindowStaleness`\n- `StageContextWindowUnavailableReason`\n- `StageContextWindowWarning`\n\nEnums:\n\n```text\nStageContextWindowCategory:\n  system_prompt\n  tools\n  mcp_tools\n  skills\n  memory\n  conversation\n  other\n\nStageContextWindowCountMethod:\n  provider_api_scaled_breakdown\n  response_usage_scaled_breakdown\n  local_estimate\n\nStageContextWindowStaleness:\n  live\n  stored\n  unavailable\n\nStageContextWindowUnavailableReason:\n  not_agent_stage\n  not_observed\n  provider_unconfigured\n```\n\nUse `available: false` for a real run/stage where Fabro has no context-window\ndata yet. Missing runs and missing stages should still return 404. For\n`available: false`, return `breakdown: []`, `warnings` explaining the gap, and\nnullable token fields.\n\nDefine context-window usage as model-visible input/context tokens only:\n\n- Include prompt input, system/developer content, tool definitions, MCP tool\n  definitions, skills, memory files, conversation history, tool results, and\n  cache input tokens when response usage is the source.\n- Exclude output tokens and reasoning tokens.\n- Do not report cost/billing totals here. Existing billing APIs own billing.\n\n## Data Model\n\nAdd projection-only, content-free context-window types to\n`lib/crates/fabro-types/src/run_projection.rs`:\n\n- `StageContextWindowProjection`\n- `StageContextWindowBreakdownProjection`\n- category/count/staleness/warning enums, shared with the API through\n  `fabro-api` replacements if the serde shape matches.\n\nExtend `StageProjection` with:\n\n```rust\n#[serde(default, skip_serializing_if = \"Option::is_none\")]\npub context_window: Option<StageContextWindowProjection>,\n```\n\nDo not store raw content. The stored snapshot may contain only:\n\n- provider and model\n- context window size\n- input token total\n- category token counts\n- count method\n- generated timestamp\n- source event sequence\n- warning codes and messages\n\n## Events\n\nAdd a typed event in `lib/crates/fabro-types/src/run_event/agent.rs` and\n`lib/crates/fabro-types/src/run_event/mod.rs`:\n\n```text\nagent.context_window.snapshot\n```\n\nEvent payload should carry the same content-free counts as the projection plus\nthe stage id. The reducer in `lib/crates/fabro-store/src/run_state.rs` should\nreplace the selected stage's `context_window` with the latest snapshot.\n\nThis event is the durable fallback for inactive stages. It should be emitted\nwhen the agent assembles or refreshes the LLM request context, before the\nprovider request is sent. If a provider response later supplies better input\nusage for the same request, emit another snapshot using\n`response_usage_scaled_breakdown`.\n\n## Live Counting Design\n\nThe agent should produce snapshots using this decision order whenever it builds\nan LLM request:\n\n1. Build a content-free local category breakdown from the same inputs used to\n   assemble the `fabro_llm::Request`.\n2. Emit an immediate `agent.context_window.snapshot` with `local_estimate` so a\n   sidebar has data even if provider counting is slow or unavailable.\n3. Attempt provider counting with\n   `Client::count_input_tokens(..., InputTokenCountPreference::PreferProvider)`\n   using the exact in-memory request. This work must not persist or log the raw\n   request.\n4. If provider count succeeds, scale the local category estimates to the\n   provider total and emit a replacement snapshot with\n   `provider_api_scaled_breakdown`.\n5. If provider count falls back or fails, keep the local snapshot and include a\n   warning code on the next emitted snapshot. Count failures must not block the\n   agent's normal LLM request.\n\nThe API endpoint should follow this decision order:\n\n1. Validate the run and stage exist.\n2. If the stage is not an agent stage, return `available: false` with\n   `unavailable_reason: not_agent_stage`.\n3. Return the latest projected `StageContextWindowProjection`.\n4. If no snapshot has ever been observed, return `available: false` with\n   `unavailable_reason: not_observed`.\n\nDo not make the HTTP server own raw LLM requests. The existing server state\ntracks live run control and durable projections, while the exact request exists\ninside the active agent session. Provider counting should therefore happen in\nthe agent/worker process at request-assembly time, and the server endpoint\nshould expose the latest durable snapshot.\n\nIf a future implementation needs user-triggered refreshes, add a separate\nworker request/response control path. Do not tunnel raw request content through\nrun events or store it in `ManagedRun`.\n\nThe live request snapshot needs to be short-lived and content-safe:\n\n- Hold raw `Request` content only inside active agent sessions.\n- Never write that raw request to run events, projection state, logs, or API\n  responses.\n- Cache provider-count results by run id, stage id, provider, model, and request\n  fingerprint or source event seq so each LLM request is counted at most once.\n- Clear the live request handle when the session/stage deactivates.\n\n## Resolved Handoff Decisions\n\n### Decision 1: Category-Aware Estimation Ownership\n\nOptions:\n\n- Agent-only estimator: build all category counts in `fabro-agent`.\n- LLM-only estimator: move the full breakdown model into `fabro-llm`.\n- Hybrid estimator: keep category ownership in `fabro-agent`, but expose small\n  reusable token-estimation helpers from `fabro-llm`.\n\nRecommendation: use the hybrid estimator.\n\nJustification:\n\n- `fabro-agent` has the category knowledge. It sees memory documents, skills,\n  MCP registration, tool registry policy, and the final session history before\n  `Session::build_request` flattens everything into a generic LLM request.\n- `fabro-llm` has the token math and provider-neutral request structures. It\n  already owns local count behavior in `token_count.rs`, so duplicating that\n  estimator in `fabro-agent` would drift.\n- `fabro-llm` should not learn Fabro-specific categories like `skills` or\n  `memory`; that would couple a provider abstraction crate to agent UI\n  semantics.\n\nImplementation guidance:\n\n- Add a `fabro-agent/src/context_window.rs` builder that owns the category\n  taxonomy and content-free snapshot assembly.\n- Expose narrow helpers from `fabro-llm::token_count`, such as tool-definition\n  and message/content-part estimators, instead of making the private estimator\n  logic public wholesale.\n- Keep the existing `Client::count_input_tokens` provider call as the\n  authoritative total when available.\n\n### Decision 2: Provider Count Location\n\nOptions:\n\n- Server-side count: store or reconstruct the exact `fabro_llm::Request` in the\n  HTTP server and call the provider from the endpoint.\n- Synchronous worker query: add a request/response control channel so the HTTP\n  server can ask the active worker for a fresh count on demand.\n- Agent-side count on request assembly: the active session counts the exact\n  request it already has and emits content-free snapshots; the endpoint returns\n  the latest projection.\n\nRecommendation: use agent-side count on request assembly for the first\nimplementation.\n\nJustification:\n\n- It satisfies the requirement that provider counting is attempted by default,\n  because every active agent request can be counted once as it is assembled.\n- It avoids moving raw prompts, memory, tool results, or message history into\n  server-managed state.\n- The existing subprocess control path is one-way JSONL for actions like steer,\n  interrupt, and pair. Adding a synchronous query path just for this read model\n  would be more complex than emitting the durable projection the UI already\n  needs.\n- It works for both subprocess and in-process runs because the agent session is\n  the common place where the exact request exists.\n\nImplementation guidance:\n\n- Emit a local snapshot immediately, then emit a provider-scaled replacement if\n  provider counting succeeds.\n- Do not delay the LLM stream on provider counting unless implementation finds\n  that provider count latency is consistently negligible. A spawned count task\n  with the cloned request is acceptable as long as it is cancelled/ignored when\n  the session closes.\n- Treat provider count errors as snapshot warnings, not stage failures.\n\n### Decision 3: Memory and Skills Attribution\n\nOptions:\n\n- Keep the current flattened system prompt and count all prompt additions as\n  `system_prompt`.\n- Refactor prompt assembly to retain component boundaries, then estimate\n  memory and skills before the final prompt string is concatenated.\n- Add origin metadata to every history message and tool result so activated\n  skill instructions can be attributed even after entering the conversation.\n\nRecommendation: refactor prompt assembly for this first slice; defer full\nhistory-origin metadata.\n\nJustification:\n\n- `assemble_system_prompt` already receives `memory` and `skills` separately,\n  then concatenates them with the core prompt. Returning component metadata from\n  that boundary is a small, local change.\n- This gives useful and accurate first-slice attribution for loaded memory\n  files and the available-skills prompt without changing the persisted\n  conversation format.\n- Activated skill instructions are harder: slash expansion becomes a user turn,\n  and `use_skill` returns a tool result. The current `Message` enum does not\n  preserve source metadata. Adding it is possible, but it is a broader history\n  serialization migration and should not block the endpoint.\n\nImplementation guidance:\n\n- Count the base/core prompt as `system_prompt`.\n- Count memory document text appended by prompt assembly as `memory`.\n- Count the available-skills section and the `use_skill` tool definition as\n  `skills`.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` in this first implementation, with a warning such as\n  `activated_skill_context_counted_as_conversation` when such activations are\n  present.\n\n### Decision 4: Tool vs MCP Tool Attribution\n\nOptions:\n\n- Split MCP tools by name prefix, such as `mcp__`.\n- Add source metadata to `RegisteredTool` / `ToolRegistry`.\n- Recompute MCP membership from `McpConnectionManager` at request time.\n\nRecommendation: add source metadata to `RegisteredTool` / `ToolRegistry`.\n\nJustification:\n\n- The current registry stores only `ToolDefinition` plus executor, so origin is\n  lost after registration.\n- Prefix-based classification matches today's naming convention but is brittle\n  and will misclassify any future native tool that shares the prefix or any MCP\n  naming change.\n- `McpConnectionManager` has source knowledge during registration, but the\n  request builder only sees the final registry and policy-filtered tool\n  definitions.\n\nImplementation guidance:\n\n- Add a small `ToolSource` enum, for example `Native`, `Mcp { server_name }`,\n  and `Skill`.\n- Set `ToolSource::Mcp` in `mcp_integration::make_mcp_tools`.\n- Set `ToolSource::Skill` for `make_use_skill_tool`.\n- Keep existing public `definitions()` behavior unchanged; add a parallel\n  method that returns definitions with source metadata for context-window\n  accounting.\n\n### Decision 5: Unavailable and Error Semantics\n\nOptions:\n\n- Return 404/409 for non-agent stages or stages with no snapshot.\n- Return 200 with `available: false` for known stages where context-window data\n  is not applicable or not observed.\n- Return partial data with warnings for provider-count failures.\n\nRecommendation: return 200 with `available: false` for known-but-unavailable\ndata, and reserve HTTP errors for missing run/stage or malformed requests.\n\nJustification:\n\n- The sidebar needs to render stable empty states without treating normal\n  projection gaps as transport errors.\n- Provider-count support varies by provider and credentials; those are data\n  quality issues, not endpoint availability issues.\n- This matches the broader best-effort contract and avoids UI retry loops when\n  a completed older run simply has no context-window snapshot.\n\nImplementation guidance:\n\n- Use 404 only for missing run or missing stage.\n- Use `available: false` + `unavailable_reason` for `not_agent_stage`,\n  `not_observed`, or `provider_unconfigured`.\n- Use `warnings` for local estimate, provider fallback, ambiguous categories,\n  and activated skill content counted as conversation.\n\nThe previous open questions are resolved by these decisions.\n\n## Implementation Units\n\n### Unit 1: OpenAPI and Generated Clients\n\nFiles:\n\n- `docs/public/api-reference/fabro-api.yaml`\n- `lib/crates/fabro-api/build.rs`\n- `lib/crates/fabro-api/tests/`\n- `lib/packages/fabro-api-client/`\n\nTasks:\n\n- Add the path and schemas under Run Internals.\n- Prefer reusing hand-written Rust projection types through\n  `with_replacement(...)` when serde shape and semantics are identical.\n- Add a `fabro-api` test proving type identity and JSON parity for any new\n  replacement.\n- Regenerate Rust and TypeScript clients.\n\nTests:\n\n- `cargo build -p fabro-api`\n- `cargo nextest run -p fabro-api`\n- `cd lib/packages/fabro-api-client && bun run generate`\n\n### Unit 2: Content-Free Snapshot Builder\n\nFiles:\n\n- `lib/crates/fabro-llm/src/token_count.rs`\n- `lib/crates/fabro-agent/src/context_window.rs` (new)\n- `lib/crates/fabro-agent/src/session.rs`\n- `lib/crates/fabro-agent/src/profiles/mod.rs`\n- `lib/crates/fabro-agent/src/tool_registry.rs`\n- `lib/crates/fabro-agent/src/mcp_integration.rs`\n- `lib/crates/fabro-agent/src/skills.rs`\n- `lib/crates/fabro-agent/src/compaction.rs`\n- `lib/crates/fabro-agent/src/lib.rs`\n\nTasks:\n\n- Build category estimates at the same boundary where `Session::build_request`\n  assembles the `fabro_llm::Request`.\n- Expose narrow reusable token-estimation helpers from `fabro-llm` rather than\n  duplicating the estimator in `fabro-agent`.\n- Refactor prompt assembly enough to retain component boundaries for core\n  system prompt, memory, available skills, and user instructions before the\n  final prompt string is concatenated.\n- Add source metadata to registered tools so the builder can split native tools,\n  MCP tools, and skill-related tools after policy filtering.\n- Classify the system prompt separately from conversation history.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` for the first implementation, with a warning when activated\n  skill context is present.\n- Emit a local snapshot immediately and a provider-scaled replacement snapshot\n  when provider counting succeeds.\n- Keep local estimates deterministic and content-free.\n\nTests:\n\n- system prompt, tools, MCP tools, skills, memory, conversation, and other\n  categories are counted into the expected buckets.\n- category totals equal the local total before provider scaling.\n- provider-scaled totals add up to the provider total.\n- snapshots do not serialize prompt text, memory contents, tool arguments, or\n  message text.\n- opaque/ambiguous inputs produce warnings rather than silent misclassification.\n- provider count failure does not fail the agent turn.\n- each request fingerprint is counted by the provider at most once.\n\n### Unit 3: Event and Projection\n\nFiles:\n\n- `lib/crates/fabro-types/src/run_event/agent.rs`\n- `lib/crates/fabro-types/src/run_event/mod.rs`\n- `lib/crates/fabro-types/src/run_projection.rs`\n- `lib/crates/fabro-store/src/run_state.rs`\n\nTasks:\n\n- Add `agent.context_window.snapshot`.\n- Emit snapshots from the agent session path when requests are assembled and\n  when later response usage improves the count.\n- Project the latest snapshot onto `StageProjection.context_window`.\n- Preserve backwards-compatible deserialization for run projections that do not\n  have the new field.\n\nTests:\n\n- event `type_name()` returns `agent.context_window.snapshot`.\n- reducer updates only the matching stage.\n- later snapshots replace earlier snapshots for the same stage.\n- old projection JSON without `context_window` still deserializes.\n\n### Unit 4: Server Endpoint\n\nFiles:\n\n- `lib/crates/fabro-server/src/server/handler/mod.rs`\n- `lib/crates/fabro-server/src/server/handler/runs.rs` or a new\n  `context_window.rs` handler module\n- `lib/crates/fabro-server/src/server/tests.rs`\n\nTasks:\n\n- Add `GET /runs/{id}/stages/{stageId}/context-window`.\n- Use the same run-scoped authorization pattern as adjacent run internals.\n- Resolve run/stage from the cached projection first for fast 404s and stored\n  fallback.\n- Return the latest projected context-window snapshot for the stage.\n- Return `available: false` for known stages where context-window data is not\n  applicable or has not been observed.\n- Return 404 only for missing run/stage. Provider count failures are represented\n  as snapshot warnings because provider counting happens in the agent.\n\nTests:\n\n- missing run returns 404.\n- missing stage returns 404.\n- non-agent stage returns 200 with `available: false`.\n- projected provider-count success returns `staleness: live` and\n  `count_method: provider_api_scaled_breakdown`.\n- inactive stage returns the latest stored projection snapshot.\n- no observed snapshot returns `available: false` with `not_observed`.\n- projected warning payloads are returned without changing HTTP status.\n\n### Unit 5: Web Query Support\n\nFiles:\n\n- `apps/fabro-web/app/lib/query-keys.ts`\n- `apps/fabro-web/app/lib/queries.ts`\n- `apps/fabro-web/app/lib/run-events.ts`\n- `apps/fabro-web/app/lib/query-keys.test.ts`\n- `apps/fabro-web/app/lib/run-events.test.tsx`\n\nTasks:\n\n- Add `queryKeys.runs.stageContextWindow(id, stageId)`.\n- Add `useRunStageContextWindow(runId, stageId)` using the generated\n  TypeScript client.\n- Invalidate the context-window key for:\n  - `agent.context_window.snapshot`\n  - stage lifecycle events for the same stage\n  - agent activity events that can change the request context\n- Keep the full sidebar UI as follow-up work, but make the hook ready for the\n  agent-node page.\n\nTests:\n\n- query key encodes run id and stage id stably.\n- snapshot event invalidates the context-window key, run events, and stage\n  events.\n- stage lifecycle events invalidate the context-window key for the selected\n  stage.\n- activity events without a stage id do not invalidate unrelated stage context\n  windows.\n\n## Security and Privacy\n\n- Do not persist raw request content to make inactive-stage provider counting\n  possible.\n- Do not log prompt, memory, tool args, or message contents while computing\n  counts.\n- Warning messages should identify count quality, not repeat provider error\n  bodies if those bodies may contain request excerpts.\n- The endpoint should expose counts and category labels only.\n\n## Validation\n\nExpected validation after implementation:\n\n```bash\ncargo build -p fabro-api\ncargo nextest run -p fabro-api -p fabro-agent -p fabro-store -p fabro-server\ncd apps/fabro-web && bun test\ncd apps/fabro-web && bun run typecheck\ncargo +nightly-2026-04-14 fmt --check --all\ngit diff --check\n```\n\n## Remaining Follow-Ups\n\n- Full sidebar visualization.\n- Optional history-origin metadata if we later want activated skill templates\n  to move from `conversation` into `skills`.\n- Optional user-triggered live refresh path if future product needs require\n  provider counting on demand rather than at request assembly time.\n",
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          "graph.goal": "# Context Window Breakdown Endpoint Plan\n\nDate: 2026-05-23\n\n## Context\n\nAgent stage pages already have enough projected data for todos, subagents,\nskills, and MCP servers through `StageProjection` in\n`lib/crates/fabro-types/src/run_projection.rs` and the reducer in\n`lib/crates/fabro-store/src/run_state.rs`. Context-window usage is different:\nFabro emits context-window warnings and compaction events today, but it does not\nstore a category breakdown of the model-visible request context.\n\n`fabro-llm` already exposes the right counting primitive:\n`Client::count_input_tokens(request, InputTokenCountPreference::PreferProvider)`\nin `lib/crates/fabro-llm/src/client.rs`. Provider adapters can call native count\nendpoints for OpenAI, Anthropic, and Gemini, and the client already falls back\nto local estimates for fallback-eligible failures.\n\n## Goal\n\nAdd a best-effort context-window API for agent stages:\n\n```text\nGET /api/v1/runs/{id}/stages/{stageId}/context-window\n```\n\nThe endpoint should return the best context-window usage Fabro can produce with\nno caller-controlled count or accuracy parameters. It may call the configured\nLLM provider by default. If provider counting is not possible, it should degrade\nto a local estimate or the latest stored snapshot instead of making the sidebar\ntreat ordinary count gaps as hard errors.\n\n## Scope\n\nIn scope:\n\n- OpenAPI contract and generated Rust/TypeScript clients.\n- Content-free context-window DTOs in the run projection.\n- A typed agent event for latest context-window snapshots.\n- Server endpoint that combines live provider counting, local estimates, and\n  stored-snapshot fallback.\n- Web query key, hook, and SSE invalidation support so the future sidebar can\n  consume the endpoint.\n\nOut of scope:\n\n- Building the full new left sidebar UI.\n- Persisting raw prompt, memory, tool arguments, or message contents for later\n  token counting.\n- Adding user-visible count-mode or accuracy knobs.\n- Retrofitting exact historical context-window counts for older completed runs.\n\nBefore implementing, read:\n\n- `docs/internal/events-strategy.md`\n- `docs/internal/error-handling-strategy.md`\n- `docs/internal/testing-strategy.md`\n\n## API Contract\n\nAdd the route under the existing Run Internals tag in\n`docs/public/api-reference/fabro-api.yaml`:\n\n```text\nGET /runs/{id}/stages/{stageId}/context-window\n```\n\nProposed response shape:\n\n```json\n{\n  \"stage_id\": \"implement@1\",\n  \"available\": true,\n  \"unavailable_reason\": null,\n  \"provider\": \"openai\",\n  \"model\": \"gpt-5.4\",\n  \"context_window_tokens\": 400000,\n  \"input_tokens\": 123456,\n  \"usage_percent\": 30.86,\n  \"count_method\": \"provider_api_scaled_breakdown\",\n  \"staleness\": \"live\",\n  \"generated_at\": \"2026-05-23T12:34:56Z\",\n  \"event_seq\": 42,\n  \"breakdown\": [\n    {\n      \"category\": \"system_prompt\",\n      \"label\": \"System prompt\",\n      \"tokens\": 30000,\n      \"usage_percent\": 7.5,\n      \"source\": \"scaled_local_estimate\"\n    }\n  ],\n  \"warnings\": []\n}\n```\n\nRequired schemas:\n\n- `StageContextWindow`\n- `StageContextWindowBreakdownItem`\n- `StageContextWindowCategory`\n- `StageContextWindowCountMethod`\n- `StageContextWindowStaleness`\n- `StageContextWindowUnavailableReason`\n- `StageContextWindowWarning`\n\nEnums:\n\n```text\nStageContextWindowCategory:\n  system_prompt\n  tools\n  mcp_tools\n  skills\n  memory\n  conversation\n  other\n\nStageContextWindowCountMethod:\n  provider_api_scaled_breakdown\n  response_usage_scaled_breakdown\n  local_estimate\n\nStageContextWindowStaleness:\n  live\n  stored\n  unavailable\n\nStageContextWindowUnavailableReason:\n  not_agent_stage\n  not_observed\n  provider_unconfigured\n```\n\nUse `available: false` for a real run/stage where Fabro has no context-window\ndata yet. Missing runs and missing stages should still return 404. For\n`available: false`, return `breakdown: []`, `warnings` explaining the gap, and\nnullable token fields.\n\nDefine context-window usage as model-visible input/context tokens only:\n\n- Include prompt input, system/developer content, tool definitions, MCP tool\n  definitions, skills, memory files, conversation history, tool results, and\n  cache input tokens when response usage is the source.\n- Exclude output tokens and reasoning tokens.\n- Do not report cost/billing totals here. Existing billing APIs own billing.\n\n## Data Model\n\nAdd projection-only, content-free context-window types to\n`lib/crates/fabro-types/src/run_projection.rs`:\n\n- `StageContextWindowProjection`\n- `StageContextWindowBreakdownProjection`\n- category/count/staleness/warning enums, shared with the API through\n  `fabro-api` replacements if the serde shape matches.\n\nExtend `StageProjection` with:\n\n```rust\n#[serde(default, skip_serializing_if = \"Option::is_none\")]\npub context_window: Option<StageContextWindowProjection>,\n```\n\nDo not store raw content. The stored snapshot may contain only:\n\n- provider and model\n- context window size\n- input token total\n- category token counts\n- count method\n- generated timestamp\n- source event sequence\n- warning codes and messages\n\n## Events\n\nAdd a typed event in `lib/crates/fabro-types/src/run_event/agent.rs` and\n`lib/crates/fabro-types/src/run_event/mod.rs`:\n\n```text\nagent.context_window.snapshot\n```\n\nEvent payload should carry the same content-free counts as the projection plus\nthe stage id. The reducer in `lib/crates/fabro-store/src/run_state.rs` should\nreplace the selected stage's `context_window` with the latest snapshot.\n\nThis event is the durable fallback for inactive stages. It should be emitted\nwhen the agent assembles or refreshes the LLM request context, before the\nprovider request is sent. If a provider response later supplies better input\nusage for the same request, emit another snapshot using\n`response_usage_scaled_breakdown`.\n\n## Live Counting Design\n\nThe agent should produce snapshots using this decision order whenever it builds\nan LLM request:\n\n1. Build a content-free local category breakdown from the same inputs used to\n   assemble the `fabro_llm::Request`.\n2. Emit an immediate `agent.context_window.snapshot` with `local_estimate` so a\n   sidebar has data even if provider counting is slow or unavailable.\n3. Attempt provider counting with\n   `Client::count_input_tokens(..., InputTokenCountPreference::PreferProvider)`\n   using the exact in-memory request. This work must not persist or log the raw\n   request.\n4. If provider count succeeds, scale the local category estimates to the\n   provider total and emit a replacement snapshot with\n   `provider_api_scaled_breakdown`.\n5. If provider count falls back or fails, keep the local snapshot and include a\n   warning code on the next emitted snapshot. Count failures must not block the\n   agent's normal LLM request.\n\nThe API endpoint should follow this decision order:\n\n1. Validate the run and stage exist.\n2. If the stage is not an agent stage, return `available: false` with\n   `unavailable_reason: not_agent_stage`.\n3. Return the latest projected `StageContextWindowProjection`.\n4. If no snapshot has ever been observed, return `available: false` with\n   `unavailable_reason: not_observed`.\n\nDo not make the HTTP server own raw LLM requests. The existing server state\ntracks live run control and durable projections, while the exact request exists\ninside the active agent session. Provider counting should therefore happen in\nthe agent/worker process at request-assembly time, and the server endpoint\nshould expose the latest durable snapshot.\n\nIf a future implementation needs user-triggered refreshes, add a separate\nworker request/response control path. Do not tunnel raw request content through\nrun events or store it in `ManagedRun`.\n\nThe live request snapshot needs to be short-lived and content-safe:\n\n- Hold raw `Request` content only inside active agent sessions.\n- Never write that raw request to run events, projection state, logs, or API\n  responses.\n- Cache provider-count results by run id, stage id, provider, model, and request\n  fingerprint or source event seq so each LLM request is counted at most once.\n- Clear the live request handle when the session/stage deactivates.\n\n## Resolved Handoff Decisions\n\n### Decision 1: Category-Aware Estimation Ownership\n\nOptions:\n\n- Agent-only estimator: build all category counts in `fabro-agent`.\n- LLM-only estimator: move the full breakdown model into `fabro-llm`.\n- Hybrid estimator: keep category ownership in `fabro-agent`, but expose small\n  reusable token-estimation helpers from `fabro-llm`.\n\nRecommendation: use the hybrid estimator.\n\nJustification:\n\n- `fabro-agent` has the category knowledge. It sees memory documents, skills,\n  MCP registration, tool registry policy, and the final session history before\n  `Session::build_request` flattens everything into a generic LLM request.\n- `fabro-llm` has the token math and provider-neutral request structures. It\n  already owns local count behavior in `token_count.rs`, so duplicating that\n  estimator in `fabro-agent` would drift.\n- `fabro-llm` should not learn Fabro-specific categories like `skills` or\n  `memory`; that would couple a provider abstraction crate to agent UI\n  semantics.\n\nImplementation guidance:\n\n- Add a `fabro-agent/src/context_window.rs` builder that owns the category\n  taxonomy and content-free snapshot assembly.\n- Expose narrow helpers from `fabro-llm::token_count`, such as tool-definition\n  and message/content-part estimators, instead of making the private estimator\n  logic public wholesale.\n- Keep the existing `Client::count_input_tokens` provider call as the\n  authoritative total when available.\n\n### Decision 2: Provider Count Location\n\nOptions:\n\n- Server-side count: store or reconstruct the exact `fabro_llm::Request` in the\n  HTTP server and call the provider from the endpoint.\n- Synchronous worker query: add a request/response control channel so the HTTP\n  server can ask the active worker for a fresh count on demand.\n- Agent-side count on request assembly: the active session counts the exact\n  request it already has and emits content-free snapshots; the endpoint returns\n  the latest projection.\n\nRecommendation: use agent-side count on request assembly for the first\nimplementation.\n\nJustification:\n\n- It satisfies the requirement that provider counting is attempted by default,\n  because every active agent request can be counted once as it is assembled.\n- It avoids moving raw prompts, memory, tool results, or message history into\n  server-managed state.\n- The existing subprocess control path is one-way JSONL for actions like steer,\n  interrupt, and pair. Adding a synchronous query path just for this read model\n  would be more complex than emitting the durable projection the UI already\n  needs.\n- It works for both subprocess and in-process runs because the agent session is\n  the common place where the exact request exists.\n\nImplementation guidance:\n\n- Emit a local snapshot immediately, then emit a provider-scaled replacement if\n  provider counting succeeds.\n- Do not delay the LLM stream on provider counting unless implementation finds\n  that provider count latency is consistently negligible. A spawned count task\n  with the cloned request is acceptable as long as it is cancelled/ignored when\n  the session closes.\n- Treat provider count errors as snapshot warnings, not stage failures.\n\n### Decision 3: Memory and Skills Attribution\n\nOptions:\n\n- Keep the current flattened system prompt and count all prompt additions as\n  `system_prompt`.\n- Refactor prompt assembly to retain component boundaries, then estimate\n  memory and skills before the final prompt string is concatenated.\n- Add origin metadata to every history message and tool result so activated\n  skill instructions can be attributed even after entering the conversation.\n\nRecommendation: refactor prompt assembly for this first slice; defer full\nhistory-origin metadata.\n\nJustification:\n\n- `assemble_system_prompt` already receives `memory` and `skills` separately,\n  then concatenates them with the core prompt. Returning component metadata from\n  that boundary is a small, local change.\n- This gives useful and accurate first-slice attribution for loaded memory\n  files and the available-skills prompt without changing the persisted\n  conversation format.\n- Activated skill instructions are harder: slash expansion becomes a user turn,\n  and `use_skill` returns a tool result. The current `Message` enum does not\n  preserve source metadata. Adding it is possible, but it is a broader history\n  serialization migration and should not block the endpoint.\n\nImplementation guidance:\n\n- Count the base/core prompt as `system_prompt`.\n- Count memory document text appended by prompt assembly as `memory`.\n- Count the available-skills section and the `use_skill` tool definition as\n  `skills`.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` in this first implementation, with a warning such as\n  `activated_skill_context_counted_as_conversation` when such activations are\n  present.\n\n### Decision 4: Tool vs MCP Tool Attribution\n\nOptions:\n\n- Split MCP tools by name prefix, such as `mcp__`.\n- Add source metadata to `RegisteredTool` / `ToolRegistry`.\n- Recompute MCP membership from `McpConnectionManager` at request time.\n\nRecommendation: add source metadata to `RegisteredTool` / `ToolRegistry`.\n\nJustification:\n\n- The current registry stores only `ToolDefinition` plus executor, so origin is\n  lost after registration.\n- Prefix-based classification matches today's naming convention but is brittle\n  and will misclassify any future native tool that shares the prefix or any MCP\n  naming change.\n- `McpConnectionManager` has source knowledge during registration, but the\n  request builder only sees the final registry and policy-filtered tool\n  definitions.\n\nImplementation guidance:\n\n- Add a small `ToolSource` enum, for example `Native`, `Mcp { server_name }`,\n  and `Skill`.\n- Set `ToolSource::Mcp` in `mcp_integration::make_mcp_tools`.\n- Set `ToolSource::Skill` for `make_use_skill_tool`.\n- Keep existing public `definitions()` behavior unchanged; add a parallel\n  method that returns definitions with source metadata for context-window\n  accounting.\n\n### Decision 5: Unavailable and Error Semantics\n\nOptions:\n\n- Return 404/409 for non-agent stages or stages with no snapshot.\n- Return 200 with `available: false` for known stages where context-window data\n  is not applicable or not observed.\n- Return partial data with warnings for provider-count failures.\n\nRecommendation: return 200 with `available: false` for known-but-unavailable\ndata, and reserve HTTP errors for missing run/stage or malformed requests.\n\nJustification:\n\n- The sidebar needs to render stable empty states without treating normal\n  projection gaps as transport errors.\n- Provider-count support varies by provider and credentials; those are data\n  quality issues, not endpoint availability issues.\n- This matches the broader best-effort contract and avoids UI retry loops when\n  a completed older run simply has no context-window snapshot.\n\nImplementation guidance:\n\n- Use 404 only for missing run or missing stage.\n- Use `available: false` + `unavailable_reason` for `not_agent_stage`,\n  `not_observed`, or `provider_unconfigured`.\n- Use `warnings` for local estimate, provider fallback, ambiguous categories,\n  and activated skill content counted as conversation.\n\nThe previous open questions are resolved by these decisions.\n\n## Implementation Units\n\n### Unit 1: OpenAPI and Generated Clients\n\nFiles:\n\n- `docs/public/api-reference/fabro-api.yaml`\n- `lib/crates/fabro-api/build.rs`\n- `lib/crates/fabro-api/tests/`\n- `lib/packages/fabro-api-client/`\n\nTasks:\n\n- Add the path and schemas under Run Internals.\n- Prefer reusing hand-written Rust projection types through\n  `with_replacement(...)` when serde shape and semantics are identical.\n- Add a `fabro-api` test proving type identity and JSON parity for any new\n  replacement.\n- Regenerate Rust and TypeScript clients.\n\nTests:\n\n- `cargo build -p fabro-api`\n- `cargo nextest run -p fabro-api`\n- `cd lib/packages/fabro-api-client && bun run generate`\n\n### Unit 2: Content-Free Snapshot Builder\n\nFiles:\n\n- `lib/crates/fabro-llm/src/token_count.rs`\n- `lib/crates/fabro-agent/src/context_window.rs` (new)\n- `lib/crates/fabro-agent/src/session.rs`\n- `lib/crates/fabro-agent/src/profiles/mod.rs`\n- `lib/crates/fabro-agent/src/tool_registry.rs`\n- `lib/crates/fabro-agent/src/mcp_integration.rs`\n- `lib/crates/fabro-agent/src/skills.rs`\n- `lib/crates/fabro-agent/src/compaction.rs`\n- `lib/crates/fabro-agent/src/lib.rs`\n\nTasks:\n\n- Build category estimates at the same boundary where `Session::build_request`\n  assembles the `fabro_llm::Request`.\n- Expose narrow reusable token-estimation helpers from `fabro-llm` rather than\n  duplicating the estimator in `fabro-agent`.\n- Refactor prompt assembly enough to retain component boundaries for core\n  system prompt, memory, available skills, and user instructions before the\n  final prompt string is concatenated.\n- Add source metadata to registered tools so the builder can split native tools,\n  MCP tools, and skill-related tools after policy filtering.\n- Classify the system prompt separately from conversation history.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` for the first implementation, with a warning when activated\n  skill context is present.\n- Emit a local snapshot immediately and a provider-scaled replacement snapshot\n  when provider counting succeeds.\n- Keep local estimates deterministic and content-free.\n\nTests:\n\n- system prompt, tools, MCP tools, skills, memory, conversation, and other\n  categories are counted into the expected buckets.\n- category totals equal the local total before provider scaling.\n- provider-scaled totals add up to the provider total.\n- snapshots do not serialize prompt text, memory contents, tool arguments, or\n  message text.\n- opaque/ambiguous inputs produce warnings rather than silent misclassification.\n- provider count failure does not fail the agent turn.\n- each request fingerprint is counted by the provider at most once.\n\n### Unit 3: Event and Projection\n\nFiles:\n\n- `lib/crates/fabro-types/src/run_event/agent.rs`\n- `lib/crates/fabro-types/src/run_event/mod.rs`\n- `lib/crates/fabro-types/src/run_projection.rs`\n- `lib/crates/fabro-store/src/run_state.rs`\n\nTasks:\n\n- Add `agent.context_window.snapshot`.\n- Emit snapshots from the agent session path when requests are assembled and\n  when later response usage improves the count.\n- Project the latest snapshot onto `StageProjection.context_window`.\n- Preserve backwards-compatible deserialization for run projections that do not\n  have the new field.\n\nTests:\n\n- event `type_name()` returns `agent.context_window.snapshot`.\n- reducer updates only the matching stage.\n- later snapshots replace earlier snapshots for the same stage.\n- old projection JSON without `context_window` still deserializes.\n\n### Unit 4: Server Endpoint\n\nFiles:\n\n- `lib/crates/fabro-server/src/server/handler/mod.rs`\n- `lib/crates/fabro-server/src/server/handler/runs.rs` or a new\n  `context_window.rs` handler module\n- `lib/crates/fabro-server/src/server/tests.rs`\n\nTasks:\n\n- Add `GET /runs/{id}/stages/{stageId}/context-window`.\n- Use the same run-scoped authorization pattern as adjacent run internals.\n- Resolve run/stage from the cached projection first for fast 404s and stored\n  fallback.\n- Return the latest projected context-window snapshot for the stage.\n- Return `available: false` for known stages where context-window data is not\n  applicable or has not been observed.\n- Return 404 only for missing run/stage. Provider count failures are represented\n  as snapshot warnings because provider counting happens in the agent.\n\nTests:\n\n- missing run returns 404.\n- missing stage returns 404.\n- non-agent stage returns 200 with `available: false`.\n- projected provider-count success returns `staleness: live` and\n  `count_method: provider_api_scaled_breakdown`.\n- inactive stage returns the latest stored projection snapshot.\n- no observed snapshot returns `available: false` with `not_observed`.\n- projected warning payloads are returned without changing HTTP status.\n\n### Unit 5: Web Query Support\n\nFiles:\n\n- `apps/fabro-web/app/lib/query-keys.ts`\n- `apps/fabro-web/app/lib/queries.ts`\n- `apps/fabro-web/app/lib/run-events.ts`\n- `apps/fabro-web/app/lib/query-keys.test.ts`\n- `apps/fabro-web/app/lib/run-events.test.tsx`\n\nTasks:\n\n- Add `queryKeys.runs.stageContextWindow(id, stageId)`.\n- Add `useRunStageContextWindow(runId, stageId)` using the generated\n  TypeScript client.\n- Invalidate the context-window key for:\n  - `agent.context_window.snapshot`\n  - stage lifecycle events for the same stage\n  - agent activity events that can change the request context\n- Keep the full sidebar UI as follow-up work, but make the hook ready for the\n  agent-node page.\n\nTests:\n\n- query key encodes run id and stage id stably.\n- snapshot event invalidates the context-window key, run events, and stage\n  events.\n- stage lifecycle events invalidate the context-window key for the selected\n  stage.\n- activity events without a stage id do not invalidate unrelated stage context\n  windows.\n\n## Security and Privacy\n\n- Do not persist raw request content to make inactive-stage provider counting\n  possible.\n- Do not log prompt, memory, tool args, or message contents while computing\n  counts.\n- Warning messages should identify count quality, not repeat provider error\n  bodies if those bodies may contain request excerpts.\n- The endpoint should expose counts and category labels only.\n\n## Validation\n\nExpected validation after implementation:\n\n```bash\ncargo build -p fabro-api\ncargo nextest run -p fabro-api -p fabro-agent -p fabro-store -p fabro-server\ncd apps/fabro-web && bun test\ncd apps/fabro-web && bun run typecheck\ncargo +nightly-2026-04-14 fmt --check --all\ngit diff --check\n```\n\n## Remaining Follow-Ups\n\n- Full sidebar visualization.\n- Optional history-origin metadata if we later want activated skill templates\n  to move from `conversation` into `skills`.\n- Optional user-triggered live refresh path if future product needs require\n  provider counting on demand rather than at request assembly time.\n",
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          "response.implement": "Implemented the context-window breakdown endpoint end-to-end.\n\n### What changed\n\n- Added OpenAPI contract for:\n  - `GET /api/v1/runs/{id}/stages/{stageId}/context-window`\n  - `StageContextWindow*` schemas/enums/warnings/breakdown DTOs.\n- Added content-free projection types in `fabro-types` and wired them through `fabro-api` replacements/tests.\n- Added typed event:\n  - `agent.context_window.snapshot`\n- Added reducer support in `fabro-store` so latest snapshots replace prior stage snapshots.\n- Added agent-side context-window accounting:\n  - local category breakdown at request assembly time\n  - async provider token count via `Client::count_input_tokens(... PreferProvider)`\n  - provider/API scaled replacement snapshots\n  - response-usage scaled replacement snapshots\n  - content-safe request fingerprinting and close-token cancellation for count tasks\n- Added tool source metadata (`Native`, `Mcp`, `Skill`) so tool definitions split into native tools, MCP tools, and skills.\n- Exposed narrow token-estimation helpers from `fabro-llm`.\n- Added server endpoint behavior:\n  - 404 for missing run/stage\n  - `available: false` for non-agent stages and unobserved agent stages\n  - stored/latest projected snapshot response with warnings preserved\n- Added web query support:\n  - `queryKeys.runs.stageContextWindow(id, stageId)`\n  - `useRunStageContextWindow(runId, stageId)`\n  - SSE invalidation for context-window snapshots, stage lifecycle, steering/activity, and todo activity.\n- Updated TypeScript generated client files manually for the new endpoint/models because the generator could not run in this environment.\n\n### Validation run\n\nPassed:\n\n- `cargo build -q -p fabro-api`\n- `cargo check -q --workspace`\n- `cargo nextest run -p fabro-types -p fabro-store -p fabro-api --no-fail-fast`\n- `cargo nextest run -p fabro-agent --no-fail-fast`\n- `cargo nextest run -p fabro-workflow --no-fail-fast`\n- `cargo nextest run -p fabro-server get_run_stage_context_window --no-fail-fast`\n- `cargo +nightly-2026-04-14 fmt --check --all`\n- `cargo +nightly-2026-04-14 clippy --workspace --all-targets -- -D warnings`\n- `git diff --check`\n- `cd apps/fabro-web && bun test app/lib/query-keys.test.ts app/lib/run-events.test.tsx`\n- `cd apps/fabro-web && bun run typecheck`\n- `cd apps/fabro-web && bun test app/lib/run-actions.test.ts`\n\nNotes:\n\n- `cd lib/packages/fabro-api-client && bun run generate` failed because the environment lacks `java`; `bunx` latest also hit a Node 18 ESM issue. I manually updated the generated TypeScript client/model files to match the OpenAPI change.\n- Full `cd apps/fabro-web && bun test` still reports failures in `run-actions.test.ts` only when run as part of the entire suite; that file passes in isolation. This appears to be pre-existing/global test-state interference, not caused by the context-window changes.\n- Full `cargo nextest run -p fabro-server` previously surfaced unrelated graph-render subprocess failures; the new context-window endpoint tests pass directly.",
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          "graph.goal": "# Context Window Breakdown Endpoint Plan\n\nDate: 2026-05-23\n\n## Context\n\nAgent stage pages already have enough projected data for todos, subagents,\nskills, and MCP servers through `StageProjection` in\n`lib/crates/fabro-types/src/run_projection.rs` and the reducer in\n`lib/crates/fabro-store/src/run_state.rs`. Context-window usage is different:\nFabro emits context-window warnings and compaction events today, but it does not\nstore a category breakdown of the model-visible request context.\n\n`fabro-llm` already exposes the right counting primitive:\n`Client::count_input_tokens(request, InputTokenCountPreference::PreferProvider)`\nin `lib/crates/fabro-llm/src/client.rs`. Provider adapters can call native count\nendpoints for OpenAI, Anthropic, and Gemini, and the client already falls back\nto local estimates for fallback-eligible failures.\n\n## Goal\n\nAdd a best-effort context-window API for agent stages:\n\n```text\nGET /api/v1/runs/{id}/stages/{stageId}/context-window\n```\n\nThe endpoint should return the best context-window usage Fabro can produce with\nno caller-controlled count or accuracy parameters. It may call the configured\nLLM provider by default. If provider counting is not possible, it should degrade\nto a local estimate or the latest stored snapshot instead of making the sidebar\ntreat ordinary count gaps as hard errors.\n\n## Scope\n\nIn scope:\n\n- OpenAPI contract and generated Rust/TypeScript clients.\n- Content-free context-window DTOs in the run projection.\n- A typed agent event for latest context-window snapshots.\n- Server endpoint that combines live provider counting, local estimates, and\n  stored-snapshot fallback.\n- Web query key, hook, and SSE invalidation support so the future sidebar can\n  consume the endpoint.\n\nOut of scope:\n\n- Building the full new left sidebar UI.\n- Persisting raw prompt, memory, tool arguments, or message contents for later\n  token counting.\n- Adding user-visible count-mode or accuracy knobs.\n- Retrofitting exact historical context-window counts for older completed runs.\n\nBefore implementing, read:\n\n- `docs/internal/events-strategy.md`\n- `docs/internal/error-handling-strategy.md`\n- `docs/internal/testing-strategy.md`\n\n## API Contract\n\nAdd the route under the existing Run Internals tag in\n`docs/public/api-reference/fabro-api.yaml`:\n\n```text\nGET /runs/{id}/stages/{stageId}/context-window\n```\n\nProposed response shape:\n\n```json\n{\n  \"stage_id\": \"implement@1\",\n  \"available\": true,\n  \"unavailable_reason\": null,\n  \"provider\": \"openai\",\n  \"model\": \"gpt-5.4\",\n  \"context_window_tokens\": 400000,\n  \"input_tokens\": 123456,\n  \"usage_percent\": 30.86,\n  \"count_method\": \"provider_api_scaled_breakdown\",\n  \"staleness\": \"live\",\n  \"generated_at\": \"2026-05-23T12:34:56Z\",\n  \"event_seq\": 42,\n  \"breakdown\": [\n    {\n      \"category\": \"system_prompt\",\n      \"label\": \"System prompt\",\n      \"tokens\": 30000,\n      \"usage_percent\": 7.5,\n      \"source\": \"scaled_local_estimate\"\n    }\n  ],\n  \"warnings\": []\n}\n```\n\nRequired schemas:\n\n- `StageContextWindow`\n- `StageContextWindowBreakdownItem`\n- `StageContextWindowCategory`\n- `StageContextWindowCountMethod`\n- `StageContextWindowStaleness`\n- `StageContextWindowUnavailableReason`\n- `StageContextWindowWarning`\n\nEnums:\n\n```text\nStageContextWindowCategory:\n  system_prompt\n  tools\n  mcp_tools\n  skills\n  memory\n  conversation\n  other\n\nStageContextWindowCountMethod:\n  provider_api_scaled_breakdown\n  response_usage_scaled_breakdown\n  local_estimate\n\nStageContextWindowStaleness:\n  live\n  stored\n  unavailable\n\nStageContextWindowUnavailableReason:\n  not_agent_stage\n  not_observed\n  provider_unconfigured\n```\n\nUse `available: false` for a real run/stage where Fabro has no context-window\ndata yet. Missing runs and missing stages should still return 404. For\n`available: false`, return `breakdown: []`, `warnings` explaining the gap, and\nnullable token fields.\n\nDefine context-window usage as model-visible input/context tokens only:\n\n- Include prompt input, system/developer content, tool definitions, MCP tool\n  definitions, skills, memory files, conversation history, tool results, and\n  cache input tokens when response usage is the source.\n- Exclude output tokens and reasoning tokens.\n- Do not report cost/billing totals here. Existing billing APIs own billing.\n\n## Data Model\n\nAdd projection-only, content-free context-window types to\n`lib/crates/fabro-types/src/run_projection.rs`:\n\n- `StageContextWindowProjection`\n- `StageContextWindowBreakdownProjection`\n- category/count/staleness/warning enums, shared with the API through\n  `fabro-api` replacements if the serde shape matches.\n\nExtend `StageProjection` with:\n\n```rust\n#[serde(default, skip_serializing_if = \"Option::is_none\")]\npub context_window: Option<StageContextWindowProjection>,\n```\n\nDo not store raw content. The stored snapshot may contain only:\n\n- provider and model\n- context window size\n- input token total\n- category token counts\n- count method\n- generated timestamp\n- source event sequence\n- warning codes and messages\n\n## Events\n\nAdd a typed event in `lib/crates/fabro-types/src/run_event/agent.rs` and\n`lib/crates/fabro-types/src/run_event/mod.rs`:\n\n```text\nagent.context_window.snapshot\n```\n\nEvent payload should carry the same content-free counts as the projection plus\nthe stage id. The reducer in `lib/crates/fabro-store/src/run_state.rs` should\nreplace the selected stage's `context_window` with the latest snapshot.\n\nThis event is the durable fallback for inactive stages. It should be emitted\nwhen the agent assembles or refreshes the LLM request context, before the\nprovider request is sent. If a provider response later supplies better input\nusage for the same request, emit another snapshot using\n`response_usage_scaled_breakdown`.\n\n## Live Counting Design\n\nThe agent should produce snapshots using this decision order whenever it builds\nan LLM request:\n\n1. Build a content-free local category breakdown from the same inputs used to\n   assemble the `fabro_llm::Request`.\n2. Emit an immediate `agent.context_window.snapshot` with `local_estimate` so a\n   sidebar has data even if provider counting is slow or unavailable.\n3. Attempt provider counting with\n   `Client::count_input_tokens(..., InputTokenCountPreference::PreferProvider)`\n   using the exact in-memory request. This work must not persist or log the raw\n   request.\n4. If provider count succeeds, scale the local category estimates to the\n   provider total and emit a replacement snapshot with\n   `provider_api_scaled_breakdown`.\n5. If provider count falls back or fails, keep the local snapshot and include a\n   warning code on the next emitted snapshot. Count failures must not block the\n   agent's normal LLM request.\n\nThe API endpoint should follow this decision order:\n\n1. Validate the run and stage exist.\n2. If the stage is not an agent stage, return `available: false` with\n   `unavailable_reason: not_agent_stage`.\n3. Return the latest projected `StageContextWindowProjection`.\n4. If no snapshot has ever been observed, return `available: false` with\n   `unavailable_reason: not_observed`.\n\nDo not make the HTTP server own raw LLM requests. The existing server state\ntracks live run control and durable projections, while the exact request exists\ninside the active agent session. Provider counting should therefore happen in\nthe agent/worker process at request-assembly time, and the server endpoint\nshould expose the latest durable snapshot.\n\nIf a future implementation needs user-triggered refreshes, add a separate\nworker request/response control path. Do not tunnel raw request content through\nrun events or store it in `ManagedRun`.\n\nThe live request snapshot needs to be short-lived and content-safe:\n\n- Hold raw `Request` content only inside active agent sessions.\n- Never write that raw request to run events, projection state, logs, or API\n  responses.\n- Cache provider-count results by run id, stage id, provider, model, and request\n  fingerprint or source event seq so each LLM request is counted at most once.\n- Clear the live request handle when the session/stage deactivates.\n\n## Resolved Handoff Decisions\n\n### Decision 1: Category-Aware Estimation Ownership\n\nOptions:\n\n- Agent-only estimator: build all category counts in `fabro-agent`.\n- LLM-only estimator: move the full breakdown model into `fabro-llm`.\n- Hybrid estimator: keep category ownership in `fabro-agent`, but expose small\n  reusable token-estimation helpers from `fabro-llm`.\n\nRecommendation: use the hybrid estimator.\n\nJustification:\n\n- `fabro-agent` has the category knowledge. It sees memory documents, skills,\n  MCP registration, tool registry policy, and the final session history before\n  `Session::build_request` flattens everything into a generic LLM request.\n- `fabro-llm` has the token math and provider-neutral request structures. It\n  already owns local count behavior in `token_count.rs`, so duplicating that\n  estimator in `fabro-agent` would drift.\n- `fabro-llm` should not learn Fabro-specific categories like `skills` or\n  `memory`; that would couple a provider abstraction crate to agent UI\n  semantics.\n\nImplementation guidance:\n\n- Add a `fabro-agent/src/context_window.rs` builder that owns the category\n  taxonomy and content-free snapshot assembly.\n- Expose narrow helpers from `fabro-llm::token_count`, such as tool-definition\n  and message/content-part estimators, instead of making the private estimator\n  logic public wholesale.\n- Keep the existing `Client::count_input_tokens` provider call as the\n  authoritative total when available.\n\n### Decision 2: Provider Count Location\n\nOptions:\n\n- Server-side count: store or reconstruct the exact `fabro_llm::Request` in the\n  HTTP server and call the provider from the endpoint.\n- Synchronous worker query: add a request/response control channel so the HTTP\n  server can ask the active worker for a fresh count on demand.\n- Agent-side count on request assembly: the active session counts the exact\n  request it already has and emits content-free snapshots; the endpoint returns\n  the latest projection.\n\nRecommendation: use agent-side count on request assembly for the first\nimplementation.\n\nJustification:\n\n- It satisfies the requirement that provider counting is attempted by default,\n  because every active agent request can be counted once as it is assembled.\n- It avoids moving raw prompts, memory, tool results, or message history into\n  server-managed state.\n- The existing subprocess control path is one-way JSONL for actions like steer,\n  interrupt, and pair. Adding a synchronous query path just for this read model\n  would be more complex than emitting the durable projection the UI already\n  needs.\n- It works for both subprocess and in-process runs because the agent session is\n  the common place where the exact request exists.\n\nImplementation guidance:\n\n- Emit a local snapshot immediately, then emit a provider-scaled replacement if\n  provider counting succeeds.\n- Do not delay the LLM stream on provider counting unless implementation finds\n  that provider count latency is consistently negligible. A spawned count task\n  with the cloned request is acceptable as long as it is cancelled/ignored when\n  the session closes.\n- Treat provider count errors as snapshot warnings, not stage failures.\n\n### Decision 3: Memory and Skills Attribution\n\nOptions:\n\n- Keep the current flattened system prompt and count all prompt additions as\n  `system_prompt`.\n- Refactor prompt assembly to retain component boundaries, then estimate\n  memory and skills before the final prompt string is concatenated.\n- Add origin metadata to every history message and tool result so activated\n  skill instructions can be attributed even after entering the conversation.\n\nRecommendation: refactor prompt assembly for this first slice; defer full\nhistory-origin metadata.\n\nJustification:\n\n- `assemble_system_prompt` already receives `memory` and `skills` separately,\n  then concatenates them with the core prompt. Returning component metadata from\n  that boundary is a small, local change.\n- This gives useful and accurate first-slice attribution for loaded memory\n  files and the available-skills prompt without changing the persisted\n  conversation format.\n- Activated skill instructions are harder: slash expansion becomes a user turn,\n  and `use_skill` returns a tool result. The current `Message` enum does not\n  preserve source metadata. Adding it is possible, but it is a broader history\n  serialization migration and should not block the endpoint.\n\nImplementation guidance:\n\n- Count the base/core prompt as `system_prompt`.\n- Count memory document text appended by prompt assembly as `memory`.\n- Count the available-skills section and the `use_skill` tool definition as\n  `skills`.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` in this first implementation, with a warning such as\n  `activated_skill_context_counted_as_conversation` when such activations are\n  present.\n\n### Decision 4: Tool vs MCP Tool Attribution\n\nOptions:\n\n- Split MCP tools by name prefix, such as `mcp__`.\n- Add source metadata to `RegisteredTool` / `ToolRegistry`.\n- Recompute MCP membership from `McpConnectionManager` at request time.\n\nRecommendation: add source metadata to `RegisteredTool` / `ToolRegistry`.\n\nJustification:\n\n- The current registry stores only `ToolDefinition` plus executor, so origin is\n  lost after registration.\n- Prefix-based classification matches today's naming convention but is brittle\n  and will misclassify any future native tool that shares the prefix or any MCP\n  naming change.\n- `McpConnectionManager` has source knowledge during registration, but the\n  request builder only sees the final registry and policy-filtered tool\n  definitions.\n\nImplementation guidance:\n\n- Add a small `ToolSource` enum, for example `Native`, `Mcp { server_name }`,\n  and `Skill`.\n- Set `ToolSource::Mcp` in `mcp_integration::make_mcp_tools`.\n- Set `ToolSource::Skill` for `make_use_skill_tool`.\n- Keep existing public `definitions()` behavior unchanged; add a parallel\n  method that returns definitions with source metadata for context-window\n  accounting.\n\n### Decision 5: Unavailable and Error Semantics\n\nOptions:\n\n- Return 404/409 for non-agent stages or stages with no snapshot.\n- Return 200 with `available: false` for known stages where context-window data\n  is not applicable or not observed.\n- Return partial data with warnings for provider-count failures.\n\nRecommendation: return 200 with `available: false` for known-but-unavailable\ndata, and reserve HTTP errors for missing run/stage or malformed requests.\n\nJustification:\n\n- The sidebar needs to render stable empty states without treating normal\n  projection gaps as transport errors.\n- Provider-count support varies by provider and credentials; those are data\n  quality issues, not endpoint availability issues.\n- This matches the broader best-effort contract and avoids UI retry loops when\n  a completed older run simply has no context-window snapshot.\n\nImplementation guidance:\n\n- Use 404 only for missing run or missing stage.\n- Use `available: false` + `unavailable_reason` for `not_agent_stage`,\n  `not_observed`, or `provider_unconfigured`.\n- Use `warnings` for local estimate, provider fallback, ambiguous categories,\n  and activated skill content counted as conversation.\n\nThe previous open questions are resolved by these decisions.\n\n## Implementation Units\n\n### Unit 1: OpenAPI and Generated Clients\n\nFiles:\n\n- `docs/public/api-reference/fabro-api.yaml`\n- `lib/crates/fabro-api/build.rs`\n- `lib/crates/fabro-api/tests/`\n- `lib/packages/fabro-api-client/`\n\nTasks:\n\n- Add the path and schemas under Run Internals.\n- Prefer reusing hand-written Rust projection types through\n  `with_replacement(...)` when serde shape and semantics are identical.\n- Add a `fabro-api` test proving type identity and JSON parity for any new\n  replacement.\n- Regenerate Rust and TypeScript clients.\n\nTests:\n\n- `cargo build -p fabro-api`\n- `cargo nextest run -p fabro-api`\n- `cd lib/packages/fabro-api-client && bun run generate`\n\n### Unit 2: Content-Free Snapshot Builder\n\nFiles:\n\n- `lib/crates/fabro-llm/src/token_count.rs`\n- `lib/crates/fabro-agent/src/context_window.rs` (new)\n- `lib/crates/fabro-agent/src/session.rs`\n- `lib/crates/fabro-agent/src/profiles/mod.rs`\n- `lib/crates/fabro-agent/src/tool_registry.rs`\n- `lib/crates/fabro-agent/src/mcp_integration.rs`\n- `lib/crates/fabro-agent/src/skills.rs`\n- `lib/crates/fabro-agent/src/compaction.rs`\n- `lib/crates/fabro-agent/src/lib.rs`\n\nTasks:\n\n- Build category estimates at the same boundary where `Session::build_request`\n  assembles the `fabro_llm::Request`.\n- Expose narrow reusable token-estimation helpers from `fabro-llm` rather than\n  duplicating the estimator in `fabro-agent`.\n- Refactor prompt assembly enough to retain component boundaries for core\n  system prompt, memory, available skills, and user instructions before the\n  final prompt string is concatenated.\n- Add source metadata to registered tools so the builder can split native tools,\n  MCP tools, and skill-related tools after policy filtering.\n- Classify the system prompt separately from conversation history.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` for the first implementation, with a warning when activated\n  skill context is present.\n- Emit a local snapshot immediately and a provider-scaled replacement snapshot\n  when provider counting succeeds.\n- Keep local estimates deterministic and content-free.\n\nTests:\n\n- system prompt, tools, MCP tools, skills, memory, conversation, and other\n  categories are counted into the expected buckets.\n- category totals equal the local total before provider scaling.\n- provider-scaled totals add up to the provider total.\n- snapshots do not serialize prompt text, memory contents, tool arguments, or\n  message text.\n- opaque/ambiguous inputs produce warnings rather than silent misclassification.\n- provider count failure does not fail the agent turn.\n- each request fingerprint is counted by the provider at most once.\n\n### Unit 3: Event and Projection\n\nFiles:\n\n- `lib/crates/fabro-types/src/run_event/agent.rs`\n- `lib/crates/fabro-types/src/run_event/mod.rs`\n- `lib/crates/fabro-types/src/run_projection.rs`\n- `lib/crates/fabro-store/src/run_state.rs`\n\nTasks:\n\n- Add `agent.context_window.snapshot`.\n- Emit snapshots from the agent session path when requests are assembled and\n  when later response usage improves the count.\n- Project the latest snapshot onto `StageProjection.context_window`.\n- Preserve backwards-compatible deserialization for run projections that do not\n  have the new field.\n\nTests:\n\n- event `type_name()` returns `agent.context_window.snapshot`.\n- reducer updates only the matching stage.\n- later snapshots replace earlier snapshots for the same stage.\n- old projection JSON without `context_window` still deserializes.\n\n### Unit 4: Server Endpoint\n\nFiles:\n\n- `lib/crates/fabro-server/src/server/handler/mod.rs`\n- `lib/crates/fabro-server/src/server/handler/runs.rs` or a new\n  `context_window.rs` handler module\n- `lib/crates/fabro-server/src/server/tests.rs`\n\nTasks:\n\n- Add `GET /runs/{id}/stages/{stageId}/context-window`.\n- Use the same run-scoped authorization pattern as adjacent run internals.\n- Resolve run/stage from the cached projection first for fast 404s and stored\n  fallback.\n- Return the latest projected context-window snapshot for the stage.\n- Return `available: false` for known stages where context-window data is not\n  applicable or has not been observed.\n- Return 404 only for missing run/stage. Provider count failures are represented\n  as snapshot warnings because provider counting happens in the agent.\n\nTests:\n\n- missing run returns 404.\n- missing stage returns 404.\n- non-agent stage returns 200 with `available: false`.\n- projected provider-count success returns `staleness: live` and\n  `count_method: provider_api_scaled_breakdown`.\n- inactive stage returns the latest stored projection snapshot.\n- no observed snapshot returns `available: false` with `not_observed`.\n- projected warning payloads are returned without changing HTTP status.\n\n### Unit 5: Web Query Support\n\nFiles:\n\n- `apps/fabro-web/app/lib/query-keys.ts`\n- `apps/fabro-web/app/lib/queries.ts`\n- `apps/fabro-web/app/lib/run-events.ts`\n- `apps/fabro-web/app/lib/query-keys.test.ts`\n- `apps/fabro-web/app/lib/run-events.test.tsx`\n\nTasks:\n\n- Add `queryKeys.runs.stageContextWindow(id, stageId)`.\n- Add `useRunStageContextWindow(runId, stageId)` using the generated\n  TypeScript client.\n- Invalidate the context-window key for:\n  - `agent.context_window.snapshot`\n  - stage lifecycle events for the same stage\n  - agent activity events that can change the request context\n- Keep the full sidebar UI as follow-up work, but make the hook ready for the\n  agent-node page.\n\nTests:\n\n- query key encodes run id and stage id stably.\n- snapshot event invalidates the context-window key, run events, and stage\n  events.\n- stage lifecycle events invalidate the context-window key for the selected\n  stage.\n- activity events without a stage id do not invalidate unrelated stage context\n  windows.\n\n## Security and Privacy\n\n- Do not persist raw request content to make inactive-stage provider counting\n  possible.\n- Do not log prompt, memory, tool args, or message contents while computing\n  counts.\n- Warning messages should identify count quality, not repeat provider error\n  bodies if those bodies may contain request excerpts.\n- The endpoint should expose counts and category labels only.\n\n## Validation\n\nExpected validation after implementation:\n\n```bash\ncargo build -p fabro-api\ncargo nextest run -p fabro-api -p fabro-agent -p fabro-store -p fabro-server\ncd apps/fabro-web && bun test\ncd apps/fabro-web && bun run typecheck\ncargo +nightly-2026-04-14 fmt --check --all\ngit diff --check\n```\n\n## Remaining Follow-Ups\n\n- Full sidebar visualization.\n- Optional history-origin metadata if we later want activated skill templates\n  to move from `conversation` into `skills`.\n- Optional user-triggered live refresh path if future product needs require\n  provider counting on demand rather than at request assembly time.\n",
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              "last_response": "Implemented the context-window breakdown endpoint end-to-end.\n\n### What changed\n\n- Added OpenAPI contract for:\n  - `GET /api/v1/runs/{id}/stages/{stageId}/context-window`\n  - `StageContextWindow*` sch",
              "response.implement": "Implemented the context-window breakdown endpoint end-to-end.\n\n### What changed\n\n- Added OpenAPI contract for:\n  - `GET /api/v1/runs/{id}/stages/{stageId}/context-window`\n  - `StageContextWindow*` schemas/enums/warnings/breakdown DTOs.\n- Added content-free projection types in `fabro-types` and wired them through `fabro-api` replacements/tests.\n- Added typed event:\n  - `agent.context_window.snapshot`\n- Added reducer support in `fabro-store` so latest snapshots replace prior stage snapshots.\n- Added agent-side context-window accounting:\n  - local category breakdown at request assembly time\n  - async provider token count via `Client::count_input_tokens(... PreferProvider)`\n  - provider/API scaled replacement snapshots\n  - response-usage scaled replacement snapshots\n  - content-safe request fingerprinting and close-token cancellation for count tasks\n- Added tool source metadata (`Native`, `Mcp`, `Skill`) so tool definitions split into native tools, MCP tools, and skills.\n- Exposed narrow token-estimation helpers from `fabro-llm`.\n- Added server endpoint behavior:\n  - 404 for missing run/stage\n  - `available: false` for non-agent stages and unobserved agent stages\n  - stored/latest projected snapshot response with warnings preserved\n- Added web query support:\n  - `queryKeys.runs.stageContextWindow(id, stageId)`\n  - `useRunStageContextWindow(runId, stageId)`\n  - SSE invalidation for context-window snapshots, stage lifecycle, steering/activity, and todo activity.\n- Updated TypeScript generated client files manually for the new endpoint/models because the generator could not run in this environment.\n\n### Validation run\n\nPassed:\n\n- `cargo build -q -p fabro-api`\n- `cargo check -q --workspace`\n- `cargo nextest run -p fabro-types -p fabro-store -p fabro-api --no-fail-fast`\n- `cargo nextest run -p fabro-agent --no-fail-fast`\n- `cargo nextest run -p fabro-workflow --no-fail-fast`\n- `cargo nextest run -p fabro-server get_run_stage_context_window --no-fail-fast`\n- `cargo +nightly-2026-04-14 fmt --check --all`\n- `cargo +nightly-2026-04-14 clippy --workspace --all-targets -- -D warnings`\n- `git diff --check`\n- `cd apps/fabro-web && bun test app/lib/query-keys.test.ts app/lib/run-events.test.tsx`\n- `cd apps/fabro-web && bun run typecheck`\n- `cd apps/fabro-web && bun test app/lib/run-actions.test.ts`\n\nNotes:\n\n- `cd lib/packages/fabro-api-client && bun run generate` failed because the environment lacks `java`; `bunx` latest also hit a Node 18 ESM issue. I manually updated the generated TypeScript client/model files to match the OpenAPI change.\n- Full `cd apps/fabro-web && bun test` still reports failures in `run-actions.test.ts` only when run as part of the entire suite; that file passes in isolation. This appears to be pre-existing/global test-state interference, not caused by the context-window changes.\n- Full `cargo nextest run -p fabro-server` previously surfaced unrelated graph-render subprocess failures; the new context-window endpoint tests pass directly."
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              "/home/daytona/workspace/fabro/lib/packages/fabro-api-client/src/models/stage-context-window-breakdown-item.ts",
              "/home/daytona/workspace/fabro/lib/packages/fabro-api-client/src/models/stage-context-window-category.ts",
              "/home/daytona/workspace/fabro/lib/packages/fabro-api-client/src/models/stage-context-window-count-method.ts",
              "/home/daytona/workspace/fabro/lib/packages/fabro-api-client/src/models/stage-context-window-projection.ts",
              "/home/daytona/workspace/fabro/lib/packages/fabro-api-client/src/models/stage-context-window-staleness.ts",
              "/home/daytona/workspace/fabro/lib/packages/fabro-api-client/src/models/stage-context-window-unavailable-reason.ts",
              "/home/daytona/workspace/fabro/lib/packages/fabro-api-client/src/models/stage-context-window-warning.ts",
              "/home/daytona/workspace/fabro/lib/packages/fabro-api-client/src/models/stage-context-window.ts"
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            "notes": "Script completed: command -v cargo >/dev/null || { curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y && sudo ln -sf $HOME/.cargo/bin/* /usr/local/bin/; }; cargo --version 2>&1",
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        "patch": "diff --git a/apps/fabro-web/app/lib/queries.ts b/apps/fabro-web/app/lib/queries.ts\nindex 187ae38ac..28e1d3565 100644\n--- a/apps/fabro-web/app/lib/queries.ts\n+++ b/apps/fabro-web/app/lib/queries.ts\n@@ -25,6 +25,7 @@ import type {\n   SandboxFileListResponse,\n   SandboxServiceListResponse,\n   ServerSettings,\n+  StageContextWindow,\n   SystemInfoResponse,\n   SystemResourcesResponse,\n   VncPreviewResponse,\n@@ -350,6 +351,16 @@ export function useRunStageEvents(id: string | undefined, stageId: string | unde\n   );\n }\n \n+export function useRunStageContextWindow(\n+  id: string | undefined,\n+  stageId: string | undefined,\n+) {\n+  return useSWR<StageContextWindow | null>(\n+    id && stageId ? queryKeys.runs.stageContextWindow(id, stageId) : null,\n+    () => apiNullableData(() => runInternalsApi.getRunStageContextWindow(id!, stageId!)),\n+  );\n+}\n+\n export function useRunEventsList(id: string | undefined) {\n   return useSWR<EventEnvelope[]>(\n     id ? queryKeys.runs.events(id, 1000) : null,\ndiff --git a/apps/fabro-web/app/lib/query-keys.test.ts b/apps/fabro-web/app/lib/query-keys.test.ts\nindex 3ad519dc7..c5ac22a03 100644\n--- a/apps/fabro-web/app/lib/query-keys.test.ts\n+++ b/apps/fabro-web/app/lib/query-keys.test.ts\n@@ -43,6 +43,12 @@ describe(\"queryKeys\", () => {\n       \"run 1\",\n       \"build step\",\n     ]);\n+    expect(queryKeys.runs.stageContextWindow(\"run 1\", \"build step@2\")).toEqual([\n+      \"runs\",\n+      \"stage-context-window\",\n+      \"run 1\",\n+      \"build step@2\",\n+    ]);\n     expect(queryKeys.runs.sandbox(\"run 1\")).toEqual([\"runs\", \"sandbox\", \"run 1\"]);\n     expect(queryKeys.system.attachUrl()).toBe(\"/api/v1/attach\");\n     expect(queryKeys.runs.attachUrl(\"run 1\")).toBe(\"/api/v1/runs/run%201/attach\");\n@@ -63,6 +69,7 @@ describe(\"queryKeys\", () => {\n       queryKeys.runs.graph(\"run-1\", \"TB\"),\n       queryKeys.runs.detail(\"run-1\"),\n       queryKeys.runs.stageEvents(\"run-1\", \"stage-1\"),\n+      queryKeys.runs.stageContextWindow(\"run-1\", \"stage-1\"),\n     ]);\n     expect(queryKeysForRunEvent(\"run-1\", \"run.title.updated\")).toEqual([\n       queryKeys.runs.detail(\"run-1\"),\n@@ -80,10 +87,21 @@ describe(\"queryKeys\", () => {\n     ]) {\n       expect(queryKeysForRunEvent(\"run-1\", event, \"stage-1\")).toEqual([\n         queryKeys.runs.stageEvents(\"run-1\", \"stage-1\"),\n+        queryKeys.runs.stageContextWindow(\"run-1\", \"stage-1\"),\n       ]);\n     }\n   });\n \n+  test(\"context-window snapshot invalidates context window, run events, and stage events\", () => {\n+    expect(\n+      queryKeysForRunEvent(\"run-1\", \"agent.context_window.snapshot\", \"stage-1\"),\n+    ).toEqual([\n+      queryKeys.runs.events(\"run-1\", 1000),\n+      queryKeys.runs.stageEvents(\"run-1\", \"stage-1\"),\n+      queryKeys.runs.stageContextWindow(\"run-1\", \"stage-1\"),\n+    ]);\n+  });\n+\n   test(\"agent activity events without a node_id invalidate nothing\", () => {\n     expect(queryKeysForRunEvent(\"run-1\", \"agent.message\")).toEqual([]);\n   });\ndiff --git a/apps/fabro-web/app/lib/query-keys.ts b/apps/fabro-web/app/lib/query-keys.ts\nindex da551a33f..b5f068190 100644\n--- a/apps/fabro-web/app/lib/query-keys.ts\n+++ b/apps/fabro-web/app/lib/query-keys.ts\n@@ -66,6 +66,8 @@ export const queryKeys = {\n     events: (id: string, limit = 1000) => [\"runs\", \"events\", id, limit] as const,\n     stageEvents: (id: string, stageId: string) =>\n       [\"runs\", \"stage-events\", id, stageId] as const,\n+    stageContextWindow: (id: string, stageId: string) =>\n+      [\"runs\", \"stage-context-window\", id, stageId] as const,\n     stageLog: (id: string, stageId: string, offset = 0, limit = 65_536) =>\n       [\"runs\", \"stage-log\", id, stageId, offset, limit] as const,\n     sandbox: (id: string) => [\"runs\", \"sandbox\", id] as const,\ndiff --git a/apps/fabro-web/app/lib/run-events.test.tsx b/apps/fabro-web/app/lib/run-events.test.tsx\nindex fd74f40b7..306ddf6b1 100644\n--- a/apps/fabro-web/app/lib/run-events.test.tsx\n+++ b/apps/fabro-web/app/lib/run-events.test.tsx\n@@ -61,6 +61,7 @@ describe(\"queryKeysForRunEvent\", () => {\n       queryKeys.runs.graph(\"run-1\", \"TB\"),\n       queryKeys.runs.detail(\"run-1\"),\n       queryKeys.runs.stageEvents(\"run-1\", \"verify@2\"),\n+      queryKeys.runs.stageContextWindow(\"run-1\", \"verify@2\"),\n     ]);\n   });\n \n@@ -68,6 +69,7 @@ describe(\"queryKeysForRunEvent\", () => {\n     expect(queryKeysForRunEvent(\"run-1\", \"agent.session.activated\", \"agent@1\")).toEqual([\n       queryKeys.runs.events(\"run-1\", 1000),\n       queryKeys.runs.stageEvents(\"run-1\", \"agent@1\"),\n+      queryKeys.runs.stageContextWindow(\"run-1\", \"agent@1\"),\n     ]);\n   });\n \n@@ -75,15 +77,28 @@ describe(\"queryKeysForRunEvent\", () => {\n     expect(queryKeysForRunEvent(\"run-1\", \"agent.interrupt.injected\", \"nap@1\")).toEqual([\n       queryKeys.runs.events(\"run-1\", 1000),\n       queryKeys.runs.stageEvents(\"run-1\", \"nap@1\"),\n+      queryKeys.runs.stageContextWindow(\"run-1\", \"nap@1\"),\n     ]);\n   });\n \n   test(\"pair messages invalidate the stage events query\", () => {\n     expect(queryKeysForRunEvent(\"run-1\", \"agent.pair.user_message\", \"nap@1\")).toEqual([\n       queryKeys.runs.stageEvents(\"run-1\", \"nap@1\"),\n+      queryKeys.runs.stageContextWindow(\"run-1\", \"nap@1\"),\n     ]);\n     expect(queryKeysForRunEvent(\"run-1\", \"agent.pair.system_message\", \"nap@1\")).toEqual([\n       queryKeys.runs.stageEvents(\"run-1\", \"nap@1\"),\n+      queryKeys.runs.stageContextWindow(\"run-1\", \"nap@1\"),\n+    ]);\n+  });\n+\n+  test(\"context-window snapshots invalidate context window, run events, and stage events\", () => {\n+    expect(\n+      queryKeysForRunEvent(\"run-1\", \"agent.context_window.snapshot\", \"agent@1\"),\n+    ).toEqual([\n+      queryKeys.runs.events(\"run-1\", 1000),\n+      queryKeys.runs.stageEvents(\"run-1\", \"agent@1\"),\n+      queryKeys.runs.stageContextWindow(\"run-1\", \"agent@1\"),\n     ]);\n   });\n \n@@ -101,6 +116,7 @@ describe(\"queryKeysForRunEvent\", () => {\n       queryKeys.runs.state(\"run-1\"),\n       queryKeys.runs.events(\"run-1\", 1000),\n       queryKeys.runs.stageEvents(\"run-1\", \"code@1\"),\n+      queryKeys.runs.stageContextWindow(\"run-1\", \"code@1\"),\n     ]);\n   });\n });\n@@ -391,4 +407,4 @@ async function waitFor(condition: () => boolean, timeoutMs = 200) {\n     await new Promise((resolve) => setTimeout(resolve, 2));\n   }\n   throw new Error(\"condition did not become true before timeout\");\n-}\n\\ No newline at end of file\n+}\ndiff --git a/apps/fabro-web/app/lib/run-events.ts b/apps/fabro-web/app/lib/run-events.ts\nindex 7ba5dd48b..7c8898257 100644\n--- a/apps/fabro-web/app/lib/run-events.ts\n+++ b/apps/fabro-web/app/lib/run-events.ts\n@@ -107,6 +107,7 @@ const TODO_EVENTS = new Set([\n   \"todo.updated\",\n   \"todo.deleted\",\n ]);\n+const CONTEXT_WINDOW_SNAPSHOT_EVENT = \"agent.context_window.snapshot\";\n \n export function queryKeysForRunEvent(\n   runId: string,\n@@ -154,6 +155,7 @@ export function queryKeysForRunEvent(\n     ];\n     if (stageId) {\n       keys.push(queryKeys.runs.stageEvents(runId, stageId));\n+      keys.push(queryKeys.runs.stageContextWindow(runId, stageId));\n     }\n     return keys;\n   }\n@@ -162,12 +164,27 @@ export function queryKeysForRunEvent(\n     const keys: Key[] = [queryKeys.runs.events(runId, 1000)];\n     if (stageId) {\n       keys.push(queryKeys.runs.stageEvents(runId, stageId));\n+      keys.push(queryKeys.runs.stageContextWindow(runId, stageId));\n+    }\n+    return keys;\n+  }\n+\n+  if (event === CONTEXT_WINDOW_SNAPSHOT_EVENT) {\n+    const keys: Key[] = [queryKeys.runs.events(runId, 1000)];\n+    if (stageId) {\n+      keys.push(queryKeys.runs.stageEvents(runId, stageId));\n+      keys.push(queryKeys.runs.stageContextWindow(runId, stageId));\n     }\n     return keys;\n   }\n \n   if (STAGE_ACTIVITY_EVENTS.has(event)) {\n-    return stageId ? [queryKeys.runs.stageEvents(runId, stageId)] : [];\n+    return stageId\n+      ? [\n+          queryKeys.runs.stageEvents(runId, stageId),\n+          queryKeys.runs.stageContextWindow(runId, stageId),\n+        ]\n+      : [];\n   }\n \n   if (TODO_EVENTS.has(event)) {\n@@ -177,6 +194,7 @@ export function queryKeysForRunEvent(\n     ];\n     if (stageId) {\n       keys.push(queryKeys.runs.stageEvents(runId, stageId));\n+      keys.push(queryKeys.runs.stageContextWindow(runId, stageId));\n     }\n     return keys;\n   }\ndiff --git a/docs/public/api-reference/fabro-api.yaml b/docs/public/api-reference/fabro-api.yaml\nindex e10d6077f..7094c1ca5 100644\n--- a/docs/public/api-reference/fabro-api.yaml\n+++ b/docs/public/api-reference/fabro-api.yaml\n@@ -3067,6 +3067,34 @@ paths:\n               schema:\n                 $ref: \"#/components/schemas/ErrorResponse\"\n \n+  /api/v1/runs/{id}/stages/{stageId}/context-window:\n+    get:\n+      operationId: getRunStageContextWindow\n+      tags: [Run Internals]\n+      summary: Get Stage Context Window\n+      description: |\n+        Returns the latest best-effort model-visible context-window usage snapshot for an agent stage.\n+        Known stages without applicable or observed data return `available: false`; missing runs or stages return 404.\n+      parameters:\n+        - $ref: \"#/components/parameters/RunId\"\n+        - $ref: \"#/components/parameters/StageId\"\n+      responses:\n+        \"200\":\n+          description: Latest context-window snapshot or a typed unavailable state.\n+          content:\n+            application/json:\n+              schema:\n+                $ref: \"#/components/schemas/StageContextWindow\"\n+        \"404\":\n+          description: Run or stage not found.\n+          headers:\n+            x-request-id:\n+              $ref: \"#/components/headers/XRequestId\"\n+          content:\n+            application/json:\n+              schema:\n+                $ref: \"#/components/schemas/ErrorResponse\"\n+\n   /api/v1/runs/{id}/artifacts:\n     get:\n       operationId: listRunArtifacts\n@@ -7850,6 +7878,221 @@ components:\n           type: string\n           format: date-time\n \n+    StageContextWindowCategory:\n+      description: Category of model-visible input/context tokens.\n+      type: string\n+      enum:\n+        - system_prompt\n+        - tools\n+        - mcp_tools\n+        - skills\n+        - memory\n+        - conversation\n+        - other\n+\n+    StageContextWindowCountMethod:\n+      description: Method used to produce the context-window token total and breakdown.\n+      type: string\n+      enum:\n+        - provider_api_scaled_breakdown\n+        - response_usage_scaled_breakdown\n+        - local_estimate\n+\n+    StageContextWindowStaleness:\n+      description: Freshness of the returned context-window data.\n+      type: string\n+      enum:\n+        - live\n+        - stored\n+        - unavailable\n+\n+    StageContextWindowUnavailableReason:\n+      description: Why context-window data is unavailable for a known run stage.\n+      type: string\n+      enum:\n+        - not_agent_stage\n+        - not_observed\n+        - provider_unconfigured\n+\n+    StageContextWindowWarning:\n+      description: Content-free warning about context-window count quality or attribution.\n+      type: object\n+      required:\n+        - code\n+        - message\n+      properties:\n+        code:\n+          type: string\n+          description: Stable warning code.\n+          example: provider_token_count_failed\n+        message:\n+          type: string\n+          description: Human-readable warning that must not include prompt, memory, message, or tool-argument content.\n+          example: provider input token counting failed; returned local estimate\n+\n+    StageContextWindowBreakdownItem:\n+      description: Token usage for one content category.\n+      type: object\n+      required:\n+        - category\n+        - label\n+        - tokens\n+        - usage_percent\n+        - source\n+      properties:\n+        category:\n+          $ref: \"#/components/schemas/StageContextWindowCategory\"\n+        label:\n+          type: string\n+          example: System prompt\n+        tokens:\n+          type: integer\n+          format: uint64\n+          minimum: 0\n+          example: 30000\n+        usage_percent:\n+          type: number\n+          format: double\n+          minimum: 0\n+          example: 7.5\n+        source:\n+          type: string\n+          description: Content-free source label for the category count.\n+          example: scaled_local_estimate\n+\n+    StageContextWindowProjection:\n+      description: Durable content-free context-window snapshot projected onto an agent stage.\n+      type: object\n+      required:\n+        - provider\n+        - model\n+        - context_window_tokens\n+        - input_tokens\n+        - usage_percent\n+        - count_method\n+        - staleness\n+        - generated_at\n+        - breakdown\n+        - warnings\n+      properties:\n+        provider:\n+          type: string\n+          example: openai\n+        model:\n+          type: string\n+          example: gpt-5.4\n+        context_window_tokens:\n+          type: integer\n+          format: uint64\n+          minimum: 0\n+          example: 400000\n+        input_tokens:\n+          type: integer\n+          format: uint64\n+          minimum: 0\n+          example: 123456\n+        usage_percent:\n+          type: number\n+          format: double\n+          minimum: 0\n+          example: 30.86\n+        count_method:\n+          $ref: \"#/components/schemas/StageContextWindowCountMethod\"\n+        staleness:\n+          $ref: \"#/components/schemas/StageContextWindowStaleness\"\n+        generated_at:\n+          type: string\n+          format: date-time\n+          example: \"2026-05-23T12:34:56Z\"\n+        event_seq:\n+          type: [\"integer\", \"null\"]\n+          format: uint32\n+          minimum: 1\n+          example: 42\n+        breakdown:\n+          type: array\n+          items:\n+            $ref: \"#/components/schemas/StageContextWindowBreakdownItem\"\n+        warnings:\n+          type: array\n+          items:\n+            $ref: \"#/components/schemas/StageContextWindowWarning\"\n+\n+    StageContextWindow:\n+      description: Best-effort context-window usage for one agent stage.\n+      type: object\n+      required:\n+        - stage_id\n+        - available\n+        - unavailable_reason\n+        - provider\n+        - model\n+        - context_window_tokens\n+        - input_tokens\n+        - usage_percent\n+        - count_method\n+        - staleness\n+        - generated_at\n+        - event_seq\n+        - breakdown\n+        - warnings\n+      properties:\n+        stage_id:\n+          type: string\n+          description: Stage ID in `node@visit` form.\n+          example: implement@1\n+        available:\n+          type: boolean\n+          description: Whether context-window data is available for this known stage.\n+        unavailable_reason:\n+          oneOf:\n+            - $ref: \"#/components/schemas/StageContextWindowUnavailableReason\"\n+            - type: \"null\"\n+        provider:\n+          type: [\"string\", \"null\"]\n+          example: openai\n+        model:\n+          type: [\"string\", \"null\"]\n+          example: gpt-5.4\n+        context_window_tokens:\n+          type: [\"integer\", \"null\"]\n+          format: uint64\n+          minimum: 0\n+          example: 400000\n+        input_tokens:\n+          type: [\"integer\", \"null\"]\n+          format: uint64\n+          minimum: 0\n+          example: 123456\n+        usage_percent:\n+          type: [\"number\", \"null\"]\n+          format: double\n+          minimum: 0\n+          example: 30.86\n+        count_method:\n+          oneOf:\n+            - $ref: \"#/components/schemas/StageContextWindowCountMethod\"\n+            - type: \"null\"\n+        staleness:\n+          $ref: \"#/components/schemas/StageContextWindowStaleness\"\n+        generated_at:\n+          type: [\"string\", \"null\"]\n+          format: date-time\n+          example: \"2026-05-23T12:34:56Z\"\n+        event_seq:\n+          type: [\"integer\", \"null\"]\n+          format: uint32\n+          minimum: 1\n+          example: 42\n+        breakdown:\n+          type: array\n+          items:\n+            $ref: \"#/components/schemas/StageContextWindowBreakdownItem\"\n+        warnings:\n+          type: array\n+          items:\n+            $ref: \"#/components/schemas/StageContextWindowWarning\"\n+\n     StageProjection:\n       description: Observable projection data for one workflow stage execution.\n       type: object\n@@ -7934,6 +8177,11 @@ components:\n           description: MCP servers observed by this stage.\n           items:\n             $ref: \"#/components/schemas/McpServerProjection\"\n+        context_window:\n+          oneOf:\n+            - $ref: \"#/components/schemas/StageContextWindowProjection\"\n+            - type: \"null\"\n+          description: Latest content-free context-window snapshot for this agent stage.\n         state:\n           $ref: \"#/components/schemas/StageState\"\n           description: Lifecycle state of the stage projection.\ndiff --git a/lib/crates/fabro-agent/src/apply_patch.rs b/lib/crates/fabro-agent/src/apply_patch.rs\nindex 7e06e6a89..331accdd5 100644\n--- a/lib/crates/fabro-agent/src/apply_patch.rs\n+++ b/lib/crates/fabro-agent/src/apply_patch.rs\n@@ -8,7 +8,7 @@ use std::sync::Arc;\n use fabro_llm::types::ToolDefinition;\n \n use crate::sandbox::Sandbox;\n-use crate::tool_registry::RegisteredTool;\n+use crate::tool_registry::{RegisteredTool, ToolSource};\n \n const APPLY_PATCH_LARK_GRAMMAR: &str = include_str!(\"apply_patch.lark\");\n \n@@ -494,6 +494,7 @@ pub fn make_apply_patch_tool() -> RegisteredTool {\n                 apply_patch_operations(&ops, ctx.env.as_ref()).await\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \ndiff --git a/lib/crates/fabro-agent/src/context_window.rs b/lib/crates/fabro-agent/src/context_window.rs\nnew file mode 100644\nindex 000000000..cb9e1dd51\n--- /dev/null\n+++ b/lib/crates/fabro-agent/src/context_window.rs\n@@ -0,0 +1,484 @@\n+use std::collections::BTreeMap;\n+\n+use chrono::Utc;\n+use fabro_llm::token_count::{\n+    estimate_message_tokens, estimate_request_control_tokens, estimate_text_tokens,\n+    estimate_tool_definition_tokens,\n+};\n+use fabro_llm::types::{Request, Role, Warning as LlmWarning};\n+use fabro_types::{\n+    StageContextWindowBreakdownProjection, StageContextWindowCategory,\n+    StageContextWindowCountMethod, StageContextWindowProjection, StageContextWindowStaleness,\n+    StageContextWindowWarning,\n+};\n+\n+use crate::memory::MemoryDocument;\n+use crate::skills::{Skill, format_skills_prompt_section};\n+use crate::tool_registry::{ToolDefinitionWithSource, ToolSource};\n+\n+const LOCAL_BREAKDOWN_SOURCE: &str = \"local_estimate\";\n+const SCALED_BREAKDOWN_SOURCE: &str = \"scaled_local_estimate\";\n+\n+#[derive(Clone, Copy)]\n+pub(crate) struct ContextWindowSnapshotInput<'a> {\n+    pub request: &'a Request,\n+    pub tools: &'a [ToolDefinitionWithSource],\n+    pub system_prompt: &'a str,\n+    pub memory: &'a [MemoryDocument],\n+    pub skills: &'a [Skill],\n+    pub activated_skill_context_observed: bool,\n+    pub provider: &'a str,\n+    pub model: &'a str,\n+    pub context_window_tokens: usize,\n+}\n+\n+#[must_use]\n+pub(crate) fn build_local_snapshot(\n+    input: ContextWindowSnapshotInput<'_>,\n+) -> StageContextWindowProjection {\n+    let mut builder = BreakdownBuilder::default();\n+    let mut warnings = Vec::new();\n+\n+    add_message_breakdown(&mut builder, &mut warnings, &input);\n+    add_tool_breakdown(&mut builder, input.tools);\n+    add_request_control_breakdown(&mut builder, &mut warnings, input.request);\n+\n+    if input.activated_skill_context_observed {\n+        warnings.push(StageContextWindowWarning {\n+            code:    \"activated_skill_context_counted_as_conversation\".to_string(),\n+            message: \"Activated skill instructions are counted as conversation in this version.\"\n+                .to_string(),\n+        });\n+    }\n+\n+    builder.into_snapshot(SnapshotMeta {\n+        provider: input.provider.to_string(),\n+        model: input.model.to_string(),\n+        context_window_tokens: u64::try_from(input.context_window_tokens).unwrap_or(u64::MAX),\n+        count_method: StageContextWindowCountMethod::LocalEstimate,\n+        staleness: StageContextWindowStaleness::Live,\n+        breakdown_source: LOCAL_BREAKDOWN_SOURCE,\n+        warnings,\n+    })\n+}\n+\n+#[must_use]\n+pub(crate) fn scaled_snapshot(\n+    local: &StageContextWindowProjection,\n+    input_tokens: u64,\n+    count_method: StageContextWindowCountMethod,\n+    warnings: Vec<StageContextWindowWarning>,\n+) -> StageContextWindowProjection {\n+    let breakdown = scale_breakdown(&local.breakdown, input_tokens, local.context_window_tokens);\n+    StageContextWindowProjection {\n+        provider: local.provider.clone(),\n+        model: local.model.clone(),\n+        context_window_tokens: local.context_window_tokens,\n+        input_tokens,\n+        usage_percent: usage_percent(input_tokens, local.context_window_tokens),\n+        count_method,\n+        staleness: StageContextWindowStaleness::Live,\n+        generated_at: Utc::now(),\n+        event_seq: None,\n+        breakdown,\n+        warnings,\n+    }\n+}\n+\n+#[must_use]\n+pub(crate) fn warnings_from_llm(warnings: &[LlmWarning]) -> Vec<StageContextWindowWarning> {\n+    warnings\n+        .iter()\n+        .map(|warning| StageContextWindowWarning {\n+            code:    warning\n+                .code\n+                .clone()\n+                .unwrap_or_else(|| \"token_count_warning\".to_string()),\n+            message: warning.message.clone(),\n+        })\n+        .collect()\n+}\n+\n+#[must_use]\n+pub(crate) fn warning(code: &str, message: &str) -> StageContextWindowWarning {\n+    StageContextWindowWarning {\n+        code:    code.to_string(),\n+        message: message.to_string(),\n+    }\n+}\n+\n+fn add_message_breakdown(\n+    builder: &mut BreakdownBuilder,\n+    warnings: &mut Vec<StageContextWindowWarning>,\n+    input: &ContextWindowSnapshotInput<'_>,\n+) {\n+    let memory_text = memory_prompt_suffix(input.memory);\n+    let skills_text = skills_prompt_suffix(input.skills);\n+    let memory_tokens = estimate_text_tokens(&memory_text);\n+    let skills_tokens = estimate_text_tokens(&skills_text);\n+    let mut system_parts_seen = false;\n+\n+    for message in &input.request.messages {\n+        let estimate = estimate_message_tokens(message);\n+        warnings.extend(warnings_from_llm(&estimate.warnings));\n+        if message.role == Role::System\n+            && !system_parts_seen\n+            && message.text() == input.system_prompt\n+        {\n+            system_parts_seen = true;\n+            let attributed_suffix = memory_tokens.saturating_add(skills_tokens);\n+            builder.add(\n+                StageContextWindowCategory::SystemPrompt,\n+                estimate.tokens.saturating_sub(attributed_suffix),\n+            );\n+            builder.add(StageContextWindowCategory::Memory, memory_tokens);\n+            builder.add(StageContextWindowCategory::Skills, skills_tokens);\n+        } else {\n+            builder.add(StageContextWindowCategory::Conversation, estimate.tokens);\n+        }\n+    }\n+}\n+\n+fn add_tool_breakdown(builder: &mut BreakdownBuilder, tools: &[ToolDefinitionWithSource]) {\n+    for tool in tools {\n+        let tokens = estimate_tool_definition_tokens(&tool.definition);\n+        match &tool.source {\n+            ToolSource::Native => builder.add(StageContextWindowCategory::Tools, tokens),\n+            ToolSource::Mcp { .. } => builder.add(StageContextWindowCategory::McpTools, tokens),\n+            ToolSource::Skill => builder.add(StageContextWindowCategory::Skills, tokens),\n+        }\n+    }\n+}\n+\n+fn add_request_control_breakdown(\n+    builder: &mut BreakdownBuilder,\n+    warnings: &mut Vec<StageContextWindowWarning>,\n+    request: &Request,\n+) {\n+    let estimate = estimate_request_control_tokens(request);\n+    warnings.extend(warnings_from_llm(&estimate.warnings));\n+    builder.add(StageContextWindowCategory::Other, estimate.tokens);\n+}\n+\n+fn memory_prompt_suffix(memory: &[MemoryDocument]) -> String {\n+    if memory.is_empty() {\n+        String::new()\n+    } else {\n+        format!(\n+            \"\\n\\n{}\",\n+            memory\n+                .iter()\n+                .map(|document| document.content.as_str())\n+                .collect::<Vec<_>>()\n+                .join(\"\\n\\n\")\n+        )\n+    }\n+}\n+\n+fn skills_prompt_suffix(skills: &[Skill]) -> String {\n+    let section = format_skills_prompt_section(skills);\n+    if section.is_empty() {\n+        String::new()\n+    } else {\n+        format!(\"\\n\\n{section}\")\n+    }\n+}\n+\n+#[derive(Default)]\n+struct BreakdownBuilder {\n+    tokens: BTreeMap<StageContextWindowCategory, u64>,\n+}\n+\n+impl BreakdownBuilder {\n+    fn add(&mut self, category: StageContextWindowCategory, tokens: usize) {\n+        if tokens == 0 {\n+            return;\n+        }\n+        let tokens = u64::try_from(tokens).unwrap_or(u64::MAX);\n+        self.tokens\n+            .entry(category)\n+            .and_modify(|existing| *existing = existing.saturating_add(tokens))\n+            .or_insert(tokens);\n+    }\n+\n+    fn into_snapshot(self, meta: SnapshotMeta) -> StageContextWindowProjection {\n+        let input_tokens = self.tokens.values().copied().sum::<u64>();\n+        let breakdown = self\n+            .tokens\n+            .into_iter()\n+            .map(|(category, tokens)| StageContextWindowBreakdownProjection {\n+                category,\n+                label: category_label(category).to_string(),\n+                tokens,\n+                usage_percent: usage_percent(tokens, meta.context_window_tokens),\n+                source: meta.breakdown_source.to_string(),\n+            })\n+            .collect();\n+        StageContextWindowProjection {\n+            provider: meta.provider,\n+            model: meta.model,\n+            context_window_tokens: meta.context_window_tokens,\n+            input_tokens,\n+            usage_percent: usage_percent(input_tokens, meta.context_window_tokens),\n+            count_method: meta.count_method,\n+            staleness: meta.staleness,\n+            generated_at: Utc::now(),\n+            event_seq: None,\n+            breakdown,\n+            warnings: meta.warnings,\n+        }\n+    }\n+}\n+\n+struct SnapshotMeta {\n+    provider:              String,\n+    model:                 String,\n+    context_window_tokens: u64,\n+    count_method:          StageContextWindowCountMethod,\n+    staleness:             StageContextWindowStaleness,\n+    breakdown_source:      &'static str,\n+    warnings:              Vec<StageContextWindowWarning>,\n+}\n+\n+fn scale_breakdown(\n+    breakdown: &[StageContextWindowBreakdownProjection],\n+    target_total: u64,\n+    context_window_tokens: u64,\n+) -> Vec<StageContextWindowBreakdownProjection> {\n+    let local_total = breakdown.iter().map(|item| item.tokens).sum::<u64>();\n+    if target_total == local_total {\n+        return breakdown\n+            .iter()\n+            .cloned()\n+            .map(|mut item| {\n+                item.source = SCALED_BREAKDOWN_SOURCE.to_string();\n+                item\n+            })\n+            .collect();\n+    }\n+    if breakdown.is_empty() || local_total == 0 {\n+        return (target_total > 0)\n+            .then(|| StageContextWindowBreakdownProjection {\n+                category:      StageContextWindowCategory::Other,\n+                label:         category_label(StageContextWindowCategory::Other).to_string(),\n+                tokens:        target_total,\n+                usage_percent: usage_percent(target_total, context_window_tokens),\n+                source:        SCALED_BREAKDOWN_SOURCE.to_string(),\n+            })\n+            .into_iter()\n+            .collect();\n+    }\n+\n+    let mut scaled = Vec::with_capacity(breakdown.len());\n+    let mut allocated = 0u64;\n+    let mut remainders = Vec::with_capacity(breakdown.len());\n+    let local_total_u128 = u128::from(local_total);\n+    for (index, item) in breakdown.iter().enumerate() {\n+        let numerator = u128::from(item.tokens).saturating_mul(u128::from(target_total));\n+        let tokens = u64::try_from(numerator / local_total_u128).unwrap_or(u64::MAX);\n+        allocated = allocated.saturating_add(tokens);\n+        remainders.push((index, numerator % local_total_u128));\n+        scaled.push(StageContextWindowBreakdownProjection {\n+            category: item.category,\n+            label: item.label.clone(),\n+            tokens,\n+            usage_percent: 0.0,\n+            source: SCALED_BREAKDOWN_SOURCE.to_string(),\n+        });\n+    }\n+\n+    let mut remaining = target_total.saturating_sub(allocated);\n+    remainders.sort_by(|left, right| right.1.cmp(&left.1).then_with(|| left.0.cmp(&right.0)));\n+    for (index, _) in remainders {\n+        if remaining == 0 {\n+            break;\n+        }\n+        scaled[index].tokens = scaled[index].tokens.saturating_add(1);\n+        remaining -= 1;\n+    }\n+    for item in &mut scaled {\n+        item.usage_percent = usage_percent(item.tokens, context_window_tokens);\n+    }\n+    scaled\n+}\n+\n+fn category_label(category: StageContextWindowCategory) -> &'static str {\n+    match category {\n+        StageContextWindowCategory::SystemPrompt => \"System prompt\",\n+        StageContextWindowCategory::Tools => \"Tools\",\n+        StageContextWindowCategory::McpTools => \"MCP tools\",\n+        StageContextWindowCategory::Skills => \"Skills\",\n+        StageContextWindowCategory::Memory => \"Memory\",\n+        StageContextWindowCategory::Conversation => \"Conversation\",\n+        StageContextWindowCategory::Other => \"Other\",\n+    }\n+}\n+\n+fn usage_percent(tokens: u64, denominator: u64) -> f64 {\n+    if denominator == 0 {\n+        0.0\n+    } else {\n+        (tokens as f64) * 100.0 / (denominator as f64)\n+    }\n+}\n+\n+#[cfg(test)]\n+mod tests {\n+    use fabro_llm::types::{Message as LlmMessage, Request, ToolChoice, ToolDefinition};\n+\n+    use super::*;\n+    use crate::tool_registry::ToolDefinitionWithSource;\n+\n+    fn request(messages: Vec<LlmMessage>, tools: Vec<ToolDefinition>) -> Request {\n+        Request {\n+            model: \"model-a\".to_string(),\n+            messages,\n+            provider: Some(\"test\".to_string()),\n+            tools: (!tools.is_empty()).then_some(tools),\n+            tool_choice: Some(ToolChoice::Auto),\n+            response_format: None,\n+            temperature: None,\n+            top_p: None,\n+            max_tokens: None,\n+            stop_sequences: None,\n+            reasoning_effort: None,\n+            speed: None,\n+            metadata: None,\n+            provider_options: None,\n+        }\n+    }\n+\n+    fn tool(name: &str, source: ToolSource) -> ToolDefinitionWithSource {\n+        ToolDefinitionWithSource {\n+            definition: ToolDefinition::function(\n+                name,\n+                format!(\"{name} description\"),\n+                serde_json::json!({\"type\": \"object\"}),\n+            ),\n+            source,\n+        }\n+    }\n+\n+    #[test]\n+    fn local_breakdown_buckets_system_memory_skills_tools_and_conversation() {\n+        let memory = vec![MemoryDocument {\n+            path:         \"/repo/AGENTS.md\".to_string(),\n+            content:      \"memory instructions\".to_string(),\n+            byte_count:   19,\n+            loaded_bytes: 19,\n+            truncated:    false,\n+        }];\n+        let skills = vec![Skill {\n+            name:        \"commit\".to_string(),\n+            description: \"Commit changes\".to_string(),\n+            template:    \"commit template\".to_string(),\n+        }];\n+        let system_prompt = format!(\n+            \"core prompt{}{}\",\n+            memory_prompt_suffix(&memory),\n+            skills_prompt_suffix(&skills)\n+        );\n+        let tools = vec![\n+            tool(\"read_file\", ToolSource::Native),\n+            tool(\"mcp__server__search\", ToolSource::Mcp {\n+                server_name: \"server\".to_string(),\n+            }),\n+            tool(\"use_skill\", ToolSource::Skill),\n+        ];\n+        let req = request(\n+            vec![\n+                LlmMessage::system(system_prompt.clone()),\n+                LlmMessage::user(\"hello\"),\n+            ],\n+            tools.iter().map(|tool| tool.definition.clone()).collect(),\n+        );\n+\n+        let snapshot = build_local_snapshot(ContextWindowSnapshotInput {\n+            request: &req,\n+            tools: &tools,\n+            system_prompt: &system_prompt,\n+            memory: &memory,\n+            skills: &skills,\n+            activated_skill_context_observed: true,\n+            provider: \"test\",\n+            model: \"model-a\",\n+            context_window_tokens: 100_000,\n+        });\n+\n+        let categories = snapshot\n+            .breakdown\n+            .iter()\n+            .map(|item| item.category)\n+            .collect::<Vec<_>>();\n+        assert!(categories.contains(&StageContextWindowCategory::SystemPrompt));\n+        assert!(categories.contains(&StageContextWindowCategory::Memory));\n+        assert!(categories.contains(&StageContextWindowCategory::Skills));\n+        assert!(categories.contains(&StageContextWindowCategory::Tools));\n+        assert!(categories.contains(&StageContextWindowCategory::McpTools));\n+        assert!(categories.contains(&StageContextWindowCategory::Conversation));\n+        assert_eq!(\n+            snapshot\n+                .breakdown\n+                .iter()\n+                .map(|item| item.tokens)\n+                .sum::<u64>(),\n+            snapshot.input_tokens\n+        );\n+        assert!(\n+            snapshot.warnings.iter().any(|warning| {\n+                warning.code == \"activated_skill_context_counted_as_conversation\"\n+            })\n+        );\n+    }\n+\n+    #[test]\n+    fn scaled_breakdown_totals_provider_count() {\n+        let local = StageContextWindowProjection {\n+            provider:              \"test\".to_string(),\n+            model:                 \"model-a\".to_string(),\n+            context_window_tokens: 1000,\n+            input_tokens:          30,\n+            usage_percent:         3.0,\n+            count_method:          StageContextWindowCountMethod::LocalEstimate,\n+            staleness:             StageContextWindowStaleness::Live,\n+            generated_at:          Utc::now(),\n+            event_seq:             None,\n+            breakdown:             vec![\n+                StageContextWindowBreakdownProjection {\n+                    category:      StageContextWindowCategory::SystemPrompt,\n+                    label:         \"System prompt\".to_string(),\n+                    tokens:        10,\n+                    usage_percent: 0.0,\n+                    source:        LOCAL_BREAKDOWN_SOURCE.to_string(),\n+                },\n+                StageContextWindowBreakdownProjection {\n+                    category:      StageContextWindowCategory::Conversation,\n+                    label:         \"Conversation\".to_string(),\n+                    tokens:        20,\n+                    usage_percent: 0.0,\n+                    source:        LOCAL_BREAKDOWN_SOURCE.to_string(),\n+                },\n+            ],\n+            warnings:              Vec::new(),\n+        };\n+\n+        let scaled = scaled_snapshot(\n+            &local,\n+            101,\n+            StageContextWindowCountMethod::ProviderApiScaledBreakdown,\n+            Vec::new(),\n+        );\n+\n+        assert_eq!(scaled.input_tokens, 101);\n+        assert_eq!(\n+            scaled.breakdown.iter().map(|item| item.tokens).sum::<u64>(),\n+            101\n+        );\n+        assert!(\n+            scaled\n+                .breakdown\n+                .iter()\n+                .all(|item| item.source == SCALED_BREAKDOWN_SOURCE)\n+        );\n+    }\n+}\ndiff --git a/lib/crates/fabro-agent/src/lib.rs b/lib/crates/fabro-agent/src/lib.rs\nindex 010004694..4ebc19fe6 100644\n--- a/lib/crates/fabro-agent/src/lib.rs\n+++ b/lib/crates/fabro-agent/src/lib.rs\n@@ -6,6 +6,7 @@ pub mod apply_patch;\n pub mod cli;\n pub mod compaction;\n pub mod config;\n+pub(crate) mod context_window;\n pub mod error;\n pub mod event;\n pub mod file_tracker;\ndiff --git a/lib/crates/fabro-agent/src/mcp_integration.rs b/lib/crates/fabro-agent/src/mcp_integration.rs\nindex 982e2cf06..e839ef4e3 100644\n--- a/lib/crates/fabro-agent/src/mcp_integration.rs\n+++ b/lib/crates/fabro-agent/src/mcp_integration.rs\n@@ -3,7 +3,7 @@ use std::sync::Arc;\n use fabro_llm::types::ToolDefinition;\n use fabro_mcp::connection_manager::{McpConnectionManager, call_result_to_string};\n \n-use crate::tool_registry::RegisteredTool;\n+use crate::tool_registry::{RegisteredTool, ToolSource};\n \n /// Create `RegisteredTool` instances for every tool exposed by connected MCP\n /// servers.\n@@ -14,6 +14,7 @@ pub fn make_mcp_tools(manager: &Arc<McpConnectionManager>) -> Vec<RegisteredTool\n         .map(|(qualified_name, info)| {\n             let mgr = Arc::clone(manager);\n             let name = qualified_name.clone();\n+            let server_name = info.server_name.clone();\n             let tool_timeout = std::time::Duration::from_mins(2);\n \n             RegisteredTool {\n@@ -34,6 +35,7 @@ pub fn make_mcp_tools(manager: &Arc<McpConnectionManager>) -> Vec<RegisteredTool\n                         call_result_to_string(&result)\n                     })\n                 }),\n+                source:     ToolSource::Mcp { server_name },\n             }\n         })\n         .collect()\ndiff --git a/lib/crates/fabro-agent/src/question_tools.rs b/lib/crates/fabro-agent/src/question_tools.rs\nindex d8351d67d..102eee803 100644\n--- a/lib/crates/fabro-agent/src/question_tools.rs\n+++ b/lib/crates/fabro-agent/src/question_tools.rs\n@@ -12,7 +12,7 @@ use serde::Deserialize;\n use serde_json::json;\n use tokio_util::sync::CancellationToken;\n \n-use crate::tool_registry::{RegisteredTool, ToolContext, ToolRegistry};\n+use crate::tool_registry::{RegisteredTool, ToolContext, ToolRegistry, ToolSource};\n \n tokio::task_local! {\n     static CURRENT_AGENT_TOOL_RUNTIME: AgentToolRuntime;\n@@ -209,6 +209,7 @@ fn make_openai_question_tool() -> RegisteredTool {\n                 format_openai_answers(&answers)\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -257,6 +258,7 @@ fn make_anthropic_question_tool() -> RegisteredTool {\n                 format_anthropic_answers(&answers)\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \ndiff --git a/lib/crates/fabro-agent/src/session.rs b/lib/crates/fabro-agent/src/session.rs\nindex de822bbbd..5ad7824a7 100644\n--- a/lib/crates/fabro-agent/src/session.rs\n+++ b/lib/crates/fabro-agent/src/session.rs\n@@ -1,4 +1,5 @@\n-use std::collections::{HashMap, VecDeque};\n+use std::collections::{HashMap, HashSet, VecDeque};\n+use std::hash::{Hash, Hasher};\n use std::sync::{Arc, Mutex, RwLock};\n use std::time::SystemTime;\n \n@@ -7,9 +8,10 @@ use fabro_llm::client::Client;\n use fabro_llm::error::ProviderErrorKind;\n use fabro_llm::generate::StreamAccumulator;\n use fabro_llm::provider::StreamEventStream;\n+use fabro_llm::token_count::{InputTokenCountMethod, InputTokenCountPreference};\n use fabro_llm::types::{\n     ContentPart, Message as LlmMessage, ReasoningEffort, Request, RetryPolicy, StreamEvent,\n-    ToolChoice,\n+    TokenCounts, ToolChoice,\n };\n use fabro_llm::{Error as LlmError, retry};\n use fabro_mcp::config::{McpServerSettings, McpTransport};\n@@ -25,6 +27,9 @@ use tracing::{debug, info, warn};\n use crate::agent_profile::AgentProfile;\n use crate::compaction::{check_context_usage, compact_context};\n use crate::config::SessionOptions;\n+use crate::context_window::{\n+    ContextWindowSnapshotInput, build_local_snapshot, scaled_snapshot, warning, warnings_from_llm,\n+};\n use crate::error::{Error, InterruptReason};\n use crate::event::Emitter;\n use crate::file_tracker::FileTracker;\n@@ -39,6 +44,7 @@ use crate::skills::{\n };\n use crate::subagent::{SubAgentCallbackEvent, SubAgentEventCallback, SubAgentManager};\n use crate::tool_execution::execute_tool_calls;\n+use crate::tool_registry::ToolDefinitionWithSource;\n use crate::types::{\n     AgentEvent, McpToolSummary, MemoryFileSummary, Message, SessionEvent, SessionState,\n     SkillActivationSource, SkillSummary,\n@@ -295,33 +301,41 @@ impl ToolEnvProvider for StaticEnvProvider {\n     }\n }\n \n+struct BuiltRequest {\n+    request: Request,\n+    tools:   Vec<ToolDefinitionWithSource>,\n+}\n+\n pub struct Session {\n-    id:                     String,\n+    id: String,\n     /// Root agent session ID for this session's agent tree. A root session\n     /// uses its own `id`; a subagent session inherits its parent's\n     /// `root_session_id` so todo tools that scope by root (Anthropic tasks)\n     /// share one list across all subagents.\n-    root_session_id:        String,\n-    config:                 SessionOptions,\n-    history:                History,\n-    event_emitter:          Emitter,\n-    state:                  SessionState,\n-    llm_client:             Client,\n-    provider_profile:       Arc<dyn AgentProfile>,\n-    sandbox:                Arc<dyn Sandbox>,\n-    control_state:          Arc<Mutex<ControlState>>,\n-    control_notify:         Arc<Notify>,\n-    followup_queue:         Arc<Mutex<VecDeque<String>>>,\n-    cancel_token:           CancellationToken,\n-    round_token:            Arc<RwLock<CancellationToken>>,\n-    interrupt_reason:       Arc<Mutex<Option<InterruptReason>>>,\n-    memory:                 Vec<MemoryDocument>,\n-    env_context:            EnvContext,\n-    skills:                 Vec<Skill>,\n-    system_prompt:          String,\n-    file_tracker:           FileTracker,\n-    tool_env_provider:      Option<Arc<dyn ToolEnvProvider>>,\n-    subagent_manager:       Option<Arc<AsyncMutex<SubAgentManager>>>,\n+    root_session_id: String,\n+    config: SessionOptions,\n+    history: History,\n+    event_emitter: Emitter,\n+    state: SessionState,\n+    llm_client: Client,\n+    provider_profile: Arc<dyn AgentProfile>,\n+    sandbox: Arc<dyn Sandbox>,\n+    control_state: Arc<Mutex<ControlState>>,\n+    control_notify: Arc<Notify>,\n+    followup_queue: Arc<Mutex<VecDeque<String>>>,\n+    cancel_token: CancellationToken,\n+    close_token: CancellationToken,\n+    round_token: Arc<RwLock<CancellationToken>>,\n+    interrupt_reason: Arc<Mutex<Option<InterruptReason>>>,\n+    memory: Vec<MemoryDocument>,\n+    env_context: EnvContext,\n+    skills: Vec<Skill>,\n+    system_prompt: String,\n+    activated_skill_context_observed: bool,\n+    context_window_counted_fingerprints: HashSet<u64>,\n+    file_tracker: FileTracker,\n+    tool_env_provider: Option<Arc<dyn ToolEnvProvider>>,\n+    subagent_manager: Option<Arc<AsyncMutex<SubAgentManager>>>,\n     completion_coordinator: Option<Arc<dyn CompletionCoordinator>>,\n }\n \n@@ -349,12 +363,15 @@ impl Session {\n             control_notify: Arc::new(Notify::new()),\n             followup_queue: Arc::new(Mutex::new(VecDeque::new())),\n             cancel_token: CancellationToken::new(),\n+            close_token: CancellationToken::new(),\n             round_token: Arc::new(RwLock::new(CancellationToken::new())),\n             interrupt_reason: Arc::new(Mutex::new(None)),\n             memory: Vec::new(),\n             env_context: EnvContext::default(),\n             skills: Vec::new(),\n             system_prompt: String::new(),\n+            activated_skill_context_observed: false,\n+            context_window_counted_fingerprints: HashSet::new(),\n             file_tracker: FileTracker::default(),\n             tool_env_provider: None,\n             subagent_manager,\n@@ -1103,6 +1120,7 @@ impl Session {\n \n     pub fn close(&mut self) -> bool {\n         let was_open = self.state != SessionState::Closed;\n+        self.close_token.cancel();\n         self.transition(SessionState::Closed);\n         was_open\n     }\n@@ -1215,6 +1233,7 @@ impl Session {\n             expand_skill(&self.skills, input).map_err(Error::InvalidState)?\n         };\n         if let Some(ref name) = expanded.skill_name {\n+            self.activated_skill_context_observed = true;\n             self.event_emitter\n                 .emit(self.id.clone(), AgentEvent::SkillActivated {\n                     skill_name: name.clone(),\n@@ -1300,7 +1319,9 @@ impl Session {\n             self.inject_task_reminder_if_needed();\n \n             // Build request\n-            let request = self.build_request();\n+            let built_request = self.build_request();\n+            let context_window_snapshot = self.emit_context_window_snapshots(&built_request);\n+            let request = built_request.request;\n \n             // Emit AssistantTextStart before LLM call\n             self.event_emitter\n@@ -1570,6 +1591,9 @@ impl Session {\n                 .cloned()\n                 .collect();\n             let usage = response.usage.clone();\n+            if let Some(local_snapshot) = context_window_snapshot.as_ref() {\n+                self.emit_response_usage_context_window_snapshot(local_snapshot, &usage);\n+            }\n \n             self.history.push(Message::Assistant {\n                 content: text.clone(),\n@@ -1651,6 +1675,12 @@ impl Session {\n             )\n             .await;\n             composite_watcher.abort();\n+            if tool_calls\n+                .iter()\n+                .any(|tool_call| tool_call.name == \"use_skill\")\n+            {\n+                self.activated_skill_context_observed = true;\n+            }\n \n             // Track file operations from tool calls\n             self.file_tracker\n@@ -1694,6 +1724,108 @@ impl Session {\n         Ok(())\n     }\n \n+    fn emit_context_window_snapshots(\n+        &mut self,\n+        built_request: &BuiltRequest,\n+    ) -> Option<fabro_types::StageContextWindowProjection> {\n+        let provider = self.provider_profile.provider_id().to_string();\n+        let model = self.provider_profile.model().to_string();\n+        let local_snapshot = build_local_snapshot(ContextWindowSnapshotInput {\n+            request: &built_request.request,\n+            tools: &built_request.tools,\n+            system_prompt: &self.system_prompt,\n+            memory: &self.memory,\n+            skills: &self.skills,\n+            activated_skill_context_observed: self.activated_skill_context_observed,\n+            provider: &provider,\n+            model: &model,\n+            context_window_tokens: self.provider_profile.context_window_size(),\n+        });\n+        self.event_emitter.emit(\n+            self.id.clone(),\n+            AgentEvent::ContextWindowSnapshot(local_snapshot.clone()),\n+        );\n+\n+        let fingerprint = request_fingerprint(&built_request.request)?;\n+        if !self.context_window_counted_fingerprints.insert(fingerprint) {\n+            return Some(local_snapshot);\n+        }\n+\n+        let client = self.llm_client.clone();\n+        let request = built_request.request.clone();\n+        let session_id = self.id.clone();\n+        let emitter = self.event_emitter.clone();\n+        let local_for_count = local_snapshot.clone();\n+        let close_token = self.close_token.clone();\n+        tokio::spawn(async move {\n+            let count_result = tokio::select! {\n+                biased;\n+                () = close_token.cancelled() => return,\n+                result = client.count_input_tokens(&request, InputTokenCountPreference::PreferProvider) => result,\n+            };\n+            if close_token.is_cancelled() {\n+                return;\n+            }\n+            let snapshot = match count_result {\n+                Ok(count) if count.method == InputTokenCountMethod::ProviderApi => {\n+                    let input_tokens = u64::try_from(count.input_tokens.max(0)).unwrap_or(u64::MAX);\n+                    scaled_snapshot(\n+                        &local_for_count,\n+                        input_tokens,\n+                        fabro_types::StageContextWindowCountMethod::ProviderApiScaledBreakdown,\n+                        warnings_from_llm(&count.warnings),\n+                    )\n+                }\n+                Ok(count) => {\n+                    let mut warnings = local_for_count.warnings.clone();\n+                    warnings.extend(warnings_from_llm(&count.warnings));\n+                    let input_tokens = u64::try_from(count.input_tokens.max(0)).unwrap_or(u64::MAX);\n+                    scaled_snapshot(\n+                        &local_for_count,\n+                        input_tokens,\n+                        fabro_types::StageContextWindowCountMethod::LocalEstimate,\n+                        warnings,\n+                    )\n+                }\n+                Err(_) => {\n+                    let mut warnings = local_for_count.warnings.clone();\n+                    warnings.push(warning(\n+                        \"provider_token_count_unavailable\",\n+                        \"provider input token counting was unavailable; retained local estimate\",\n+                    ));\n+                    let mut snapshot = local_for_count.clone();\n+                    snapshot.warnings = warnings;\n+                    snapshot\n+                }\n+            };\n+            emitter.emit(session_id, AgentEvent::ContextWindowSnapshot(snapshot));\n+        });\n+\n+        Some(local_snapshot)\n+    }\n+\n+    fn emit_response_usage_context_window_snapshot(\n+        &self,\n+        local_snapshot: &fabro_types::StageContextWindowProjection,\n+        usage: &TokenCounts,\n+    ) {\n+        let input_tokens = usage\n+            .input_tokens\n+            .saturating_add(usage.cache_read_tokens)\n+            .saturating_add(usage.cache_write_tokens);\n+        if input_tokens <= 0 {\n+            return;\n+        }\n+        let snapshot = scaled_snapshot(\n+            local_snapshot,\n+            u64::try_from(input_tokens).unwrap_or(u64::MAX),\n+            fabro_types::StageContextWindowCountMethod::ResponseUsageScaledBreakdown,\n+            local_snapshot.warnings.clone(),\n+        );\n+        self.event_emitter\n+            .emit(self.id.clone(), AgentEvent::ContextWindowSnapshot(snapshot));\n+    }\n+\n     async fn compact_if_needed(&mut self) {\n         let Some(estimate) = check_context_usage(\n             &self.system_prompt,\n@@ -1788,23 +1920,27 @@ impl Session {\n         }\n     }\n \n-    fn build_request(&self) -> Request {\n+    fn build_request(&self) -> BuiltRequest {\n         let mut messages = Vec::new();\n         if !self.system_prompt.trim().is_empty() {\n             messages.push(LlmMessage::system(self.system_prompt.clone()));\n         }\n         messages.extend(self.history.convert_to_messages());\n \n-        let tools = self\n+        let tools_with_source = self\n             .provider_profile\n             .tool_registry()\n-            .definitions_for_policy(\n+            .definitions_with_source_for_policy(\n                 self.config.tool_access_policy.as_deref(),\n                 self.config.tool_exposure_mode,\n             );\n+        let tools: Vec<_> = tools_with_source\n+            .iter()\n+            .map(|tool| tool.definition.clone())\n+            .collect();\n         let has_tools = !tools.is_empty();\n \n-        Request {\n+        let request = Request {\n             model: self.provider_profile.model().to_string(),\n             messages,\n             provider: Some(self.provider_profile.provider_id().to_string()),\n@@ -1826,6 +1962,10 @@ impl Session {\n             speed: self.config.speed,\n             metadata: None,\n             provider_options: None,\n+        };\n+        BuiltRequest {\n+            request,\n+            tools: tools_with_source,\n         }\n     }\n \n@@ -1854,6 +1994,13 @@ const fn is_auth_error(err: &LlmError) -> bool {\n     )\n }\n \n+fn request_fingerprint(request: &Request) -> Option<u64> {\n+    let bytes = serde_json::to_vec(request).ok()?;\n+    let mut hasher = std::collections::hash_map::DefaultHasher::new();\n+    bytes.hash(&mut hasher);\n+    Some(hasher.finish())\n+}\n+\n /// Best-effort kill of a sandbox MCP server process group. Used when\n /// `start_sandbox_mcp_server` is cancelled after spawning a detached\n /// `setsid` child but before reporting readiness. Errors from the sandbox\n@@ -1892,7 +2039,7 @@ mod tests {\n     use crate::skills::{Skill, make_use_skill_tool};\n     use crate::subagent::SubAgentStatus;\n     use crate::test_support::*;\n-    use crate::tool_registry::{RegisteredTool, ToolContext, ToolRegistry};\n+    use crate::tool_registry::{RegisteredTool, ToolContext, ToolRegistry, ToolSource};\n \n     struct NamedToolAccessPolicy {\n         decisions: Vec<(&'static str, ToolAccess)>,\n@@ -1921,6 +2068,7 @@ mod tests {\n                 parameters:  serde_json::json!({\"type\": \"object\"}),\n             },\n             executor:   Arc::new(|_args, _ctx| Box::pin(async { Ok(\"ok\".to_string()) })),\n+            source:     ToolSource::Native,\n         }\n     }\n \n@@ -2118,6 +2266,7 @@ mod tests {\n                     Ok(\"recorded\".to_string())\n                 })\n             }),\n+            source:     ToolSource::Native,\n         };\n \n         let mut registry = ToolRegistry::new();\n@@ -2568,6 +2717,7 @@ mod tests {\n                     Ok(\"done\".to_string())\n                 })\n             }),\n+            source:     ToolSource::Native,\n         };\n \n         let mut registry = ToolRegistry::new();\n@@ -2844,6 +2994,7 @@ mod tests {\n             executor:   Arc::new(|_args, _ctx| {\n                 Box::pin(async move { Ok(\"should not reach\".to_string()) })\n             }),\n+            source:     ToolSource::Native,\n         });\n \n         let responses = vec![\n@@ -2885,6 +3036,7 @@ mod tests {\n             executor:   Arc::new(|_args, _ctx| {\n                 Box::pin(async move { Ok(\"tool executed\".to_string()) })\n             }),\n+            source:     ToolSource::Native,\n         });\n \n         let responses = vec![\n@@ -4001,7 +4153,7 @@ mod tests {\n     async fn compaction_includes_structured_prompt_and_file_tracking() {\n         use fabro_llm::types::ToolDefinition;\n \n-        use crate::tool_registry::RegisteredTool;\n+        use crate::tool_registry::{RegisteredTool, ToolSource};\n \n         // Provider that captures complete() requests (compaction) while returning\n         // canned responses for stream() calls.\n@@ -4043,6 +4195,7 @@ mod tests {\n             executor:   Arc::new(|_args, _ctx| {\n                 Box::pin(async move { Ok(\"file contents\".to_string()) })\n             }),\n+            source:     ToolSource::Native,\n         };\n \n         let mut registry = ToolRegistry::new();\n@@ -4277,6 +4430,7 @@ mod tests {\n                     Ok(\"cancelled\".to_string())\n                 })\n             }),\n+            source:     ToolSource::Native,\n         };\n         let mut registry = ToolRegistry::new();\n         registry.register(slow_tool);\ndiff --git a/lib/crates/fabro-agent/src/skills.rs b/lib/crates/fabro-agent/src/skills.rs\nindex 98ac6def2..fe380bb28 100644\n--- a/lib/crates/fabro-agent/src/skills.rs\n+++ b/lib/crates/fabro-agent/src/skills.rs\n@@ -5,7 +5,7 @@ use tokio_util::sync::CancellationToken;\n \n use crate::error::{Error, InterruptReason};\n use crate::sandbox::Sandbox;\n-use crate::tool_registry::RegisteredTool;\n+use crate::tool_registry::{RegisteredTool, ToolSource};\n use crate::tools::required_str;\n use crate::types::{AgentEvent, SkillActivationSource};\n \n@@ -192,6 +192,7 @@ pub fn make_use_skill_tool(skills: Arc<Vec<Skill>>) -> RegisteredTool {\n                 Ok(skill.template.clone())\n             })\n         }),\n+        source:     ToolSource::Skill,\n     }\n }\n \ndiff --git a/lib/crates/fabro-agent/src/subagent.rs b/lib/crates/fabro-agent/src/subagent.rs\nindex 10318fbe3..0c68694c4 100644\n--- a/lib/crates/fabro-agent/src/subagent.rs\n+++ b/lib/crates/fabro-agent/src/subagent.rs\n@@ -8,7 +8,7 @@ use tokio_util::sync::CancellationToken;\n \n use crate::error::Error;\n use crate::session::Session;\n-use crate::tool_registry::RegisteredTool;\n+use crate::tool_registry::{RegisteredTool, ToolSource};\n use crate::tools::required_str;\n use crate::types::{AgentEvent, Message, SessionEvent};\n \n@@ -359,6 +359,7 @@ pub fn make_spawn_agent_tool(\n                     .map_err(|e| e.to_string())\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -394,6 +395,7 @@ pub fn make_send_input_tool(manager: Arc<AsyncMutex<SubAgentManager>>) -> Regist\n                 Ok(format!(\"Message sent to agent {agent_id}\"))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -426,6 +428,7 @@ pub fn make_wait_tool(manager: Arc<AsyncMutex<SubAgentManager>>) -> RegisteredTo\n                 ))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -455,6 +458,7 @@ pub fn make_close_agent_tool(manager: Arc<AsyncMutex<SubAgentManager>>) -> Regis\n                 Ok(format!(\"Agent {agent_id} closed\"))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \ndiff --git a/lib/crates/fabro-agent/src/test_support.rs b/lib/crates/fabro-agent/src/test_support.rs\nindex 4d2d4cf46..51aac4233 100644\n--- a/lib/crates/fabro-agent/src/test_support.rs\n+++ b/lib/crates/fabro-agent/src/test_support.rs\n@@ -19,7 +19,7 @@ use crate::profiles::EnvContext;\n use crate::sandbox::*;\n use crate::session::Session;\n use crate::skills::{Skill, format_skills_prompt_section};\n-use crate::tool_registry::{RegisteredTool, ToolRegistry};\n+use crate::tool_registry::{RegisteredTool, ToolRegistry, ToolSource};\n \n // --- TestProfile ---\n \n@@ -283,6 +283,7 @@ pub fn make_echo_tool() -> RegisteredTool {\n                 Ok(format!(\"echo: {text}\"))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -297,6 +298,7 @@ pub fn make_error_tool() -> RegisteredTool {\n         executor:   Arc::new(|_args, _ctx| {\n             Box::pin(async move { Err(\"tool execution failed\".to_string()) })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \ndiff --git a/lib/crates/fabro-agent/src/todo_tools.rs b/lib/crates/fabro-agent/src/todo_tools.rs\nindex 3fac8de10..b6bc87421 100644\n--- a/lib/crates/fabro-agent/src/todo_tools.rs\n+++ b/lib/crates/fabro-agent/src/todo_tools.rs\n@@ -17,7 +17,7 @@ use fabro_types::{TodoListKind, TodoProjection, TodoStatus, TodoUpdatedProps};\n use serde_json::Value;\n \n use crate::todo_runtime::TodoRuntime;\n-use crate::tool_registry::{RegisteredTool, ToolContext};\n+use crate::tool_registry::{RegisteredTool, ToolContext, ToolSource};\n \n /// Compute the OpenAI plan scope (`openai_plan:<session_id>`). Returns an\n /// error string the model can see if no session ID is bound to the call.\n@@ -207,6 +207,7 @@ pub fn make_update_plan_tool(runtime: Arc<TodoRuntime>) -> RegisteredTool {\n                 Ok(\"Plan updated\".to_string())\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -331,6 +332,7 @@ pub fn make_task_create_tool(runtime: Arc<TodoRuntime>) -> RegisteredTool {\n                 Ok(format!(\"Task #{task_id} created successfully: {subject}\"))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -399,6 +401,7 @@ pub fn make_task_update_tool(runtime: Arc<TodoRuntime>) -> RegisteredTool {\n                 }\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -435,6 +438,7 @@ pub fn make_task_get_tool(runtime: Arc<TodoRuntime>) -> RegisteredTool {\n                 Ok(format_task_details(todo))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -489,6 +493,7 @@ pub fn make_task_list_tool(runtime: Arc<TodoRuntime>) -> RegisteredTool {\n                 Ok(out.trim_end().to_string())\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \ndiff --git a/lib/crates/fabro-agent/src/tool_execution.rs b/lib/crates/fabro-agent/src/tool_execution.rs\nindex c55b9ce3d..2c77b3a43 100644\n--- a/lib/crates/fabro-agent/src/tool_execution.rs\n+++ b/lib/crates/fabro-agent/src/tool_execution.rs\n@@ -571,7 +571,7 @@ mod tests {\n     };\n     use crate::read_before_write_sandbox::ReadBeforeWriteSandbox;\n     use crate::test_support::MutableMockSandbox;\n-    use crate::tool_registry::{RegisteredTool, ToolContext, ToolRegistry};\n+    use crate::tool_registry::{RegisteredTool, ToolContext, ToolRegistry, ToolSource};\n     use crate::tools::{\n         make_edit_file_tool, make_grep_tool, make_read_file_tool, make_write_file_tool,\n     };\n@@ -619,6 +619,7 @@ mod tests {\n                     Ok(format!(\"echo: {text}\"))\n                 })\n             }),\n+            source:     ToolSource::Native,\n         }\n     }\n \n@@ -632,6 +633,7 @@ mod tests {\n             executor:   Arc::new(|_args: serde_json::Value, _ctx: ToolContext| {\n                 Box::pin(async move { Err(\"tool failed\".to_string()) })\n             }),\n+            source:     ToolSource::Native,\n         }\n     }\n \n@@ -994,6 +996,7 @@ mod tests {\n                     Ok(\"wrote\".to_string())\n                 })\n             }),\n+            source:     ToolSource::Native,\n         });\n         let config = SessionOptions {\n             tool_access_policy: Some(Arc::new(NamedPolicy::new([(\n@@ -1048,6 +1051,7 @@ mod tests {\n                     Ok(\"ran\".to_string())\n                 })\n             }),\n+            source:     ToolSource::Native,\n         });\n         let config = SessionOptions {\n             tool_access_policy: Some(Arc::new(NamedPolicy::new([(\ndiff --git a/lib/crates/fabro-agent/src/tool_registry.rs b/lib/crates/fabro-agent/src/tool_registry.rs\nindex c4b3837e9..df2be273e 100644\n--- a/lib/crates/fabro-agent/src/tool_registry.rs\n+++ b/lib/crates/fabro-agent/src/tool_registry.rs\n@@ -65,6 +65,23 @@ pub type ToolExecutor = Arc<\n pub struct RegisteredTool {\n     pub definition: ToolDefinition,\n     pub executor:   ToolExecutor,\n+    pub source:     ToolSource,\n+}\n+\n+#[derive(Clone, Debug, Default, PartialEq, Eq)]\n+pub enum ToolSource {\n+    #[default]\n+    Native,\n+    Mcp {\n+        server_name: String,\n+    },\n+    Skill,\n+}\n+\n+#[derive(Clone)]\n+pub struct ToolDefinitionWithSource {\n+    pub definition: ToolDefinition,\n+    pub source:     ToolSource,\n }\n \n pub struct ToolRegistry {\n@@ -97,6 +114,17 @@ impl ToolRegistry {\n         self.tools.values().map(|t| t.definition.clone()).collect()\n     }\n \n+    #[must_use]\n+    pub fn definitions_with_source(&self) -> Vec<ToolDefinitionWithSource> {\n+        self.tools\n+            .values()\n+            .map(|tool| ToolDefinitionWithSource {\n+                definition: tool.definition.clone(),\n+                source:     tool.source.clone(),\n+            })\n+            .collect()\n+    }\n+\n     #[must_use]\n     pub fn definitions_for_policy(\n         &self,\n@@ -118,6 +146,30 @@ impl ToolRegistry {\n             .collect()\n     }\n \n+    #[must_use]\n+    pub fn definitions_with_source_for_policy(\n+        &self,\n+        policy: Option<&dyn ToolAccessPolicy>,\n+        exposure_mode: ToolExposureMode,\n+    ) -> Vec<ToolDefinitionWithSource> {\n+        let Some(policy) = policy else {\n+            return self.definitions_with_source();\n+        };\n+\n+        self.tools\n+            .values()\n+            .filter(|tool| {\n+                policy\n+                    .access_for_tool(&tool.definition.name)\n+                    .is_exposed(exposure_mode)\n+            })\n+            .map(|tool| ToolDefinitionWithSource {\n+                definition: tool.definition.clone(),\n+                source:     tool.source.clone(),\n+            })\n+            .collect()\n+    }\n+\n     #[must_use]\n     pub fn names(&self) -> Vec<String> {\n         self.tools.keys().cloned().collect()\n@@ -169,6 +221,7 @@ mod tests {\n                 parameters:  serde_json::json!({\"type\": \"object\"}),\n             },\n             executor:   Arc::new(|_args, _ctx| Box::pin(async { Ok(\"ok\".into()) })),\n+            source:     ToolSource::Native,\n         }\n     }\n \n@@ -213,6 +266,7 @@ mod tests {\n                 parameters:  serde_json::json!({}),\n             },\n             executor:   Arc::new(|_args, _ctx| Box::pin(async { Ok(\"v1\".into()) })),\n+            source:     ToolSource::Native,\n         });\n         registry.register(RegisteredTool {\n             definition: ToolDefinition {\n@@ -221,6 +275,7 @@ mod tests {\n                 parameters:  serde_json::json!({}),\n             },\n             executor:   Arc::new(|_args, _ctx| Box::pin(async { Ok(\"v2\".into()) })),\n+            source:     ToolSource::Native,\n         });\n \n         let tool = registry.get(\"tool_a\").unwrap();\ndiff --git a/lib/crates/fabro-agent/src/tools.rs b/lib/crates/fabro-agent/src/tools.rs\nindex 352cf0965..2f032a2b6 100644\n--- a/lib/crates/fabro-agent/src/tools.rs\n+++ b/lib/crates/fabro-agent/src/tools.rs\n@@ -10,7 +10,7 @@ use futures::{StreamExt, stream};\n \n use crate::config::SessionOptions;\n use crate::sandbox::GrepOptions;\n-use crate::tool_registry::{RegisteredTool, ToolRegistry};\n+use crate::tool_registry::{RegisteredTool, ToolRegistry, ToolSource};\n \n const MAX_WEB_FETCH_BYTES: usize = 100 * 1024;\n const MAX_READ_MANY_FILES_CONCURRENCY: usize = 8;\n@@ -111,6 +111,7 @@ pub fn make_read_file_tool() -> RegisteredTool {\n                 Ok(content)\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -141,6 +142,7 @@ pub fn make_write_file_tool() -> RegisteredTool {\n                 Ok(format!(\"Successfully wrote to {file_path}\"))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -200,6 +202,7 @@ pub fn make_edit_file_tool() -> RegisteredTool {\n                 Ok(format!(\"Successfully edited {file_path}\"))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -270,6 +273,7 @@ pub fn make_shell_tool_with_config(config: &SessionOptions) -> RegisteredTool {\n                 Ok(output)\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -334,6 +338,7 @@ pub fn make_grep_tool() -> RegisteredTool {\n                 Ok(results.join(\"\\n\"))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -365,6 +370,7 @@ pub fn make_glob_tool() -> RegisteredTool {\n                 Ok(results.join(\"\\n\"))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -426,6 +432,7 @@ pub(crate) fn make_read_many_files_tool() -> RegisteredTool {\n                 Ok(output)\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -467,6 +474,7 @@ pub(crate) fn make_list_dir_tool() -> RegisteredTool {\n                 Ok(lines.join(\"\\n\"))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -578,6 +586,7 @@ fn make_web_search_tool_with_api_key(api_key: Option<String>) -> RegisteredTool\n                 Ok(format_brave_results(&body))\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \n@@ -679,6 +688,7 @@ pub(crate) fn make_web_fetch_tool(summarizer: Option<WebFetchSummarizer>) -> Reg\n                 }\n             })\n         }),\n+        source: ToolSource::Native,\n     }\n }\n \ndiff --git a/lib/crates/fabro-agent/src/types.rs b/lib/crates/fabro-agent/src/types.rs\nindex a15b8ae20..9aa1a014c 100644\n--- a/lib/crates/fabro-agent/src/types.rs\n+++ b/lib/crates/fabro-agent/src/types.rs\n@@ -4,7 +4,7 @@ use chrono::{DateTime, Utc};\n use fabro_llm::Error as LlmError;\n use fabro_llm::types::{ContentPart, ThinkingData, TokenCounts, ToolCall, ToolResult};\n use fabro_model::ModelRef;\n-use fabro_types::SessionMessage;\n+use fabro_types::{SessionMessage, StageContextWindowProjection};\n use serde::de::DeserializeOwned;\n use serde::{Deserialize, Serialize};\n \n@@ -304,6 +304,7 @@ pub enum AgentEvent {\n         delay_secs: f64,\n         error:      LlmError,\n     },\n+    ContextWindowSnapshot(StageContextWindowProjection),\n     SubAgentSpawned {\n         agent_id: String,\n         depth:    usize,\n@@ -503,6 +504,17 @@ impl AgentEvent {\n                     \"LLM request failed, retrying\"\n                 );\n             }\n+            Self::ContextWindowSnapshot(snapshot) => {\n+                debug!(\n+                    session_id,\n+                    provider = snapshot.provider.as_str(),\n+                    model = snapshot.model.as_str(),\n+                    input_tokens = snapshot.input_tokens,\n+                    context_window_tokens = snapshot.context_window_tokens,\n+                    count_method = %snapshot.count_method,\n+                    \"Context window snapshot\"\n+                );\n+            }\n             Self::SubAgentSpawned {\n                 agent_id,\n                 depth,\ndiff --git a/lib/crates/fabro-api/build.rs b/lib/crates/fabro-api/build.rs\nindex 0998e3c31..ba908a05b 100644\n--- a/lib/crates/fabro-api/build.rs\n+++ b/lib/crates/fabro-api/build.rs\n@@ -371,6 +371,42 @@ fn main() {\n             \"fabro_types::AgentMcpToolSummary\",\n             &[],\n         ),\n+        (\"StageContextWindow\", \"fabro_types::StageContextWindow\", &[]),\n+        (\n+            \"StageContextWindowProjection\",\n+            \"fabro_types::StageContextWindowProjection\",\n+            &[],\n+        ),\n+        (\n+            \"StageContextWindowBreakdownItem\",\n+            \"fabro_types::StageContextWindowBreakdownItem\",\n+            &[],\n+        ),\n+        (\n+            \"StageContextWindowCategory\",\n+            \"fabro_types::StageContextWindowCategory\",\n+            &[],\n+        ),\n+        (\n+            \"StageContextWindowCountMethod\",\n+            \"fabro_types::StageContextWindowCountMethod\",\n+            &[],\n+        ),\n+        (\n+            \"StageContextWindowStaleness\",\n+            \"fabro_types::StageContextWindowStaleness\",\n+            &[],\n+        ),\n+        (\n+            \"StageContextWindowUnavailableReason\",\n+            \"fabro_types::StageContextWindowUnavailableReason\",\n+            &[],\n+        ),\n+        (\n+            \"StageContextWindowWarning\",\n+            \"fabro_types::StageContextWindowWarning\",\n+            &[],\n+        ),\n         (\"SecretMetadata\", \"fabro_types::SecretMetadata\", &[]),\n         (\"InterviewOption\", \"fabro_types::InterviewOption\", &[]),\n         (\ndiff --git a/lib/crates/fabro-api/src/lib.rs b/lib/crates/fabro-api/src/lib.rs\nindex c08f1d182..346d0f036 100644\n--- a/lib/crates/fabro-api/src/lib.rs\n+++ b/lib/crates/fabro-api/src/lib.rs\n@@ -49,9 +49,13 @@ pub mod types {\n         SandboxService, SandboxServiceListResponse, SandboxState, SandboxTimestamps,\n         SecretMetadata, SecretType, ServerSettings, SessionDetail, SessionId, SessionMessage,\n         SessionRecord, SessionStatus, SessionSummary, SessionTurn, SkillsProjection,\n-        StageCompletion, StageHandler, StageModelUsage, StageOutcome, StageProjection, StageState,\n-        SubAgentProjection, SubAgentStatus, SystemActorKind, TodoListProjection, TurnId,\n-        UserPrincipal, WorkflowSettings,\n+        StageCompletion, StageContextWindow, StageContextWindowBreakdownItem,\n+        StageContextWindowBreakdownProjection, StageContextWindowCategory,\n+        StageContextWindowCountMethod, StageContextWindowProjection, StageContextWindowStaleness,\n+        StageContextWindowUnavailableReason, StageContextWindowWarning, StageHandler,\n+        StageModelUsage, StageOutcome, StageProjection, StageState, SubAgentProjection,\n+        SubAgentStatus, SystemActorKind, TodoListProjection, TurnId, UserPrincipal,\n+        WorkflowSettings,\n     };\n \n     pub use crate::generated::types::*;\ndiff --git a/lib/crates/fabro-api/tests/stage_projection_round_trip.rs b/lib/crates/fabro-api/tests/stage_projection_round_trip.rs\nindex 87f9afd95..ff27814d8 100644\n--- a/lib/crates/fabro-api/tests/stage_projection_round_trip.rs\n+++ b/lib/crates/fabro-api/tests/stage_projection_round_trip.rs\n@@ -5,13 +5,24 @@ use fabro_api::types::{\n     AgentSkillActivationSource as ApiAgentSkillActivationSource,\n     AgentSkillSummary as ApiAgentSkillSummary, McpServerProjection as ApiMcpServerProjection,\n     McpServerStatus as ApiMcpServerStatus, SkillsProjection as ApiSkillsProjection,\n+    StageContextWindow as ApiStageContextWindow,\n+    StageContextWindowBreakdownItem as ApiStageContextWindowBreakdownItem,\n+    StageContextWindowCategory as ApiStageContextWindowCategory,\n+    StageContextWindowCountMethod as ApiStageContextWindowCountMethod,\n+    StageContextWindowProjection as ApiStageContextWindowProjection,\n+    StageContextWindowStaleness as ApiStageContextWindowStaleness,\n+    StageContextWindowUnavailableReason as ApiStageContextWindowUnavailableReason,\n+    StageContextWindowWarning as ApiStageContextWindowWarning,\n     StageProjection as ApiStageProjection, SubAgentProjection as ApiSubAgentProjection,\n     SubAgentStatus as ApiSubAgentStatus, TodoListProjection as ApiTodoListProjection,\n };\n use fabro_types::{\n     ActivatedSkill, AgentMcpToolSummary, AgentSkillActivationSource, AgentSkillSummary,\n-    McpServerProjection, McpServerStatus, SkillsProjection, StageProjection, SubAgentProjection,\n-    SubAgentStatus, TodoListKind, TodoListProjection,\n+    McpServerProjection, McpServerStatus, SkillsProjection, StageContextWindow,\n+    StageContextWindowBreakdownItem, StageContextWindowCategory, StageContextWindowCountMethod,\n+    StageContextWindowProjection, StageContextWindowStaleness, StageContextWindowUnavailableReason,\n+    StageContextWindowWarning, StageProjection, SubAgentProjection, SubAgentStatus, TodoListKind,\n+    TodoListProjection,\n };\n use serde_json::json;\n \n@@ -32,6 +43,15 @@ fn stage_projection_reuses_nested_agent_state_types() {\n     assert_same_type::<ApiMcpServerProjection, McpServerProjection>();\n     assert_same_type::<ApiMcpServerStatus, McpServerStatus>();\n     assert_same_type::<ApiAgentMcpToolSummary, AgentMcpToolSummary>();\n+    assert_same_type::<ApiStageContextWindow, StageContextWindow>();\n+    assert_same_type::<ApiStageContextWindowProjection, StageContextWindowProjection>();\n+    assert_same_type::<ApiStageContextWindowBreakdownItem, StageContextWindowBreakdownItem>();\n+    assert_same_type::<ApiStageContextWindowCategory, StageContextWindowCategory>();\n+    assert_same_type::<ApiStageContextWindowCountMethod, StageContextWindowCountMethod>();\n+    assert_same_type::<ApiStageContextWindowStaleness, StageContextWindowStaleness>();\n+    assert_same_type::<ApiStageContextWindowUnavailableReason, StageContextWindowUnavailableReason>(\n+    );\n+    assert_same_type::<ApiStageContextWindowWarning, StageContextWindowWarning>();\n }\n \n #[test]\n@@ -128,6 +148,27 @@ fn stage_projection_round_trips_representative_json() {\n                 }\n             }\n         ],\n+        \"context_window\": {\n+            \"provider\": \"openai\",\n+            \"model\": \"gpt-5.4\",\n+            \"context_window_tokens\": 400000,\n+            \"input_tokens\": 123456,\n+            \"usage_percent\": 30.864,\n+            \"count_method\": \"provider_api_scaled_breakdown\",\n+            \"staleness\": \"live\",\n+            \"generated_at\": \"2026-05-23T12:34:56Z\",\n+            \"event_seq\": 42,\n+            \"breakdown\": [\n+                {\n+                    \"category\": \"system_prompt\",\n+                    \"label\": \"System prompt\",\n+                    \"tokens\": 30000,\n+                    \"usage_percent\": 7.5,\n+                    \"source\": \"scaled_local_estimate\"\n+                }\n+            ],\n+            \"warnings\": []\n+        },\n         \"state\": \"succeeded\"\n     });\n \n@@ -135,6 +176,44 @@ fn stage_projection_round_trips_representative_json() {\n     assert_eq!(serde_json::to_value(state).unwrap(), value);\n }\n \n+#[test]\n+fn stage_context_window_response_round_trips_representative_json() {\n+    let value = json!({\n+        \"stage_id\": \"implement@1\",\n+        \"available\": true,\n+        \"unavailable_reason\": null,\n+        \"provider\": \"openai\",\n+        \"model\": \"gpt-5.4\",\n+        \"context_window_tokens\": 400000,\n+        \"input_tokens\": 123456,\n+        \"usage_percent\": 30.864,\n+        \"count_method\": \"provider_api_scaled_breakdown\",\n+        \"staleness\": \"live\",\n+        \"generated_at\": \"2026-05-23T12:34:56Z\",\n+        \"event_seq\": 42,\n+        \"breakdown\": [\n+            {\n+                \"category\": \"system_prompt\",\n+                \"label\": \"System prompt\",\n+                \"tokens\": 30000,\n+                \"usage_percent\": 7.5,\n+                \"source\": \"scaled_local_estimate\"\n+            }\n+        ],\n+        \"warnings\": [\n+            {\n+                \"code\": \"local_token_estimate\",\n+                \"message\": \"input token count is a local estimate\"\n+            }\n+        ]\n+    });\n+\n+    let response: StageContextWindow = serde_json::from_value(value.clone()).unwrap();\n+    let api_response: ApiStageContextWindow = serde_json::from_value(value.clone()).unwrap();\n+    assert_eq!(api_response, response);\n+    assert_eq!(serde_json::to_value(response).unwrap(), value);\n+}\n+\n #[test]\n fn nested_agent_state_types_match_openapi_json_shape() {\n     let todo_list = TodoListProjection::new(TodoListKind::OpenAiPlan, \"openai_plan:ses_root\");\ndiff --git a/lib/crates/fabro-llm/src/token_count.rs b/lib/crates/fabro-llm/src/token_count.rs\nindex 46fb12f2c..e3bd8445b 100644\n--- a/lib/crates/fabro-llm/src/token_count.rs\n+++ b/lib/crates/fabro-llm/src/token_count.rs\n@@ -37,52 +37,26 @@ pub struct InputTokenCount {\n     pub warnings:     Vec<Warning>,\n }\n \n+#[derive(Debug, Clone, Default, PartialEq, Eq)]\n+pub struct LocalTokenEstimate {\n+    pub tokens:   usize,\n+    pub warnings: Vec<Warning>,\n+}\n+\n #[must_use]\n pub fn estimate_input_tokens(request: &Request, provider: impl Into<String>) -> InputTokenCount {\n     let mut estimator = Estimator::default();\n     let mut tokens = 0usize;\n \n     for message in &request.messages {\n-        tokens += 4;\n-        tokens += estimate_text_tokens(message.role_name());\n-        if let Some(name) = &message.name {\n-            tokens += estimate_text_tokens(name);\n-        }\n-        if let Some(tool_call_id) = &message.tool_call_id {\n-            tokens += estimate_text_tokens(tool_call_id);\n-        }\n-        for part in &message.content {\n-            tokens += 1 + estimator.estimate_content_part(part);\n-        }\n+        tokens += estimator.estimate_message(message);\n     }\n \n     if let Some(tools) = &request.tools {\n         tokens += tools.iter().map(estimate_tool).sum::<usize>();\n     }\n \n-    if let Some(tool_choice) = &request.tool_choice {\n-        if let Ok(value) = serde_json::to_value(tool_choice) {\n-            tokens += estimate_json_tokens(&value);\n-        }\n-    }\n-\n-    if let Some(response_format) = &request.response_format {\n-        if let Ok(value) = serde_json::to_value(response_format) {\n-            tokens += estimate_json_tokens(&value);\n-        }\n-    }\n-\n-    if let Some(reasoning_effort) = request.reasoning_effort {\n-        tokens += estimate_text_tokens(reasoning_effort.to_string().as_str());\n-    }\n-\n-    if let Some(provider_options) = &request.provider_options {\n-        tokens += estimate_json_tokens(provider_options);\n-        estimator.warn(\n-            PROVIDER_OPTIONS_ESTIMATE_WARNING,\n-            \"provider options estimated from JSON\",\n-        );\n-    }\n+    tokens += estimator.estimate_request_controls(request);\n \n     estimator.warn(\n         LOCAL_ESTIMATE_WARNING,\n@@ -108,6 +82,41 @@ pub fn estimate_json_tokens(value: &serde_json::Value) -> usize {\n     serde_json::to_string(value).map_or(0, |json| json.len().div_ceil(4))\n }\n \n+#[must_use]\n+pub fn estimate_message_tokens(message: &Message) -> LocalTokenEstimate {\n+    let mut estimator = Estimator::default();\n+    let tokens = estimator.estimate_message(message);\n+    LocalTokenEstimate {\n+        tokens,\n+        warnings: estimator.warnings,\n+    }\n+}\n+\n+#[must_use]\n+pub fn estimate_content_part_tokens(part: &ContentPart) -> LocalTokenEstimate {\n+    let mut estimator = Estimator::default();\n+    let tokens = estimator.estimate_content_part(part);\n+    LocalTokenEstimate {\n+        tokens,\n+        warnings: estimator.warnings,\n+    }\n+}\n+\n+#[must_use]\n+pub fn estimate_tool_definition_tokens(tool: &ToolDefinition) -> usize {\n+    estimate_tool(tool)\n+}\n+\n+#[must_use]\n+pub fn estimate_request_control_tokens(request: &Request) -> LocalTokenEstimate {\n+    let mut estimator = Estimator::default();\n+    let tokens = estimator.estimate_request_controls(request);\n+    LocalTokenEstimate {\n+        tokens,\n+        warnings: estimator.warnings,\n+    }\n+}\n+\n #[derive(Default)]\n struct Estimator {\n     warnings:   Vec<Warning>,\n@@ -115,6 +124,48 @@ struct Estimator {\n }\n \n impl Estimator {\n+    fn estimate_message(&mut self, message: &Message) -> usize {\n+        let mut tokens = 4 + estimate_text_tokens(message.role_name());\n+        if let Some(name) = &message.name {\n+            tokens += estimate_text_tokens(name);\n+        }\n+        if let Some(tool_call_id) = &message.tool_call_id {\n+            tokens += estimate_text_tokens(tool_call_id);\n+        }\n+        for part in &message.content {\n+            tokens += 1 + self.estimate_content_part(part);\n+        }\n+        tokens\n+    }\n+\n+    fn estimate_request_controls(&mut self, request: &Request) -> usize {\n+        let mut tokens = 0;\n+        if let Some(tool_choice) = &request.tool_choice {\n+            if let Ok(value) = serde_json::to_value(tool_choice) {\n+                tokens += estimate_json_tokens(&value);\n+            }\n+        }\n+\n+        if let Some(response_format) = &request.response_format {\n+            if let Ok(value) = serde_json::to_value(response_format) {\n+                tokens += estimate_json_tokens(&value);\n+            }\n+        }\n+\n+        if let Some(reasoning_effort) = request.reasoning_effort {\n+            tokens += estimate_text_tokens(reasoning_effort.to_string().as_str());\n+        }\n+\n+        if let Some(provider_options) = &request.provider_options {\n+            tokens += estimate_json_tokens(provider_options);\n+            self.warn(\n+                PROVIDER_OPTIONS_ESTIMATE_WARNING,\n+                \"provider options estimated from JSON\",\n+            );\n+        }\n+        tokens\n+    }\n+\n     fn estimate_content_part(&mut self, part: &ContentPart) -> usize {\n         match part {\n             ContentPart::Text(text) => estimate_text_tokens(text),\ndiff --git a/lib/crates/fabro-server/src/server/handler/mod.rs b/lib/crates/fabro-server/src/server/handler/mod.rs\nindex a99ccfce4..2f07ff0bf 100644\n--- a/lib/crates/fabro-server/src/server/handler/mod.rs\n+++ b/lib/crates/fabro-server/src/server/handler/mod.rs\n@@ -65,6 +65,10 @@ pub(super) fn demo_routes() -> Router<Arc<AppState>> {\n             \"/runs/{id}/stages/{stageId}/events\",\n             get(demo::get_stage_events),\n         )\n+        .route(\n+            \"/runs/{id}/stages/{stageId}/context-window\",\n+            get(not_implemented),\n+        )\n         .route(\n             \"/runs/{id}/stages/{stageId}/artifacts\",\n             get(not_implemented).post(not_implemented),\ndiff --git a/lib/crates/fabro-server/src/server/handler/runs.rs b/lib/crates/fabro-server/src/server/handler/runs.rs\nindex fcf6e5a09..a8aff0c48 100644\n--- a/lib/crates/fabro-server/src/server/handler/runs.rs\n+++ b/lib/crates/fabro-server/src/server/handler/runs.rs\n@@ -20,8 +20,9 @@ use fabro_config::Storage;\n use fabro_interview::AnswerSubmission;\n use fabro_llm::client::Client as LlmClient;\n use fabro_types::{\n-    Principal, RunClientProvenance, RunId, RunProvenance, RunServerProvenance, SystemActorKind,\n-    parse_blob_ref,\n+    Principal, RunClientProvenance, RunId, RunProvenance, RunServerProvenance, StageContextWindow,\n+    StageContextWindowStaleness, StageContextWindowUnavailableReason, StageHandler,\n+    StageModelUsage, StageProjection, StageState, SystemActorKind, parse_blob_ref,\n };\n use fabro_util::version::FABRO_VERSION;\n use fabro_workflow::command_log::{command_log_path, read_json_string_blob, read_log_slice};\n@@ -33,13 +34,13 @@ use tracing::info;\n use super::super::{\n     AppState, ListResponse, PaginationParams, RunExecutionMode, answer_from_request,\n     api_question_from_pending_interview, default_page_limit, delete_run_internal,\n-    load_pending_interview, managed_run, paginate_items, parse_run_id_path, reject_if_archived,\n-    resolve_interp_string, submit_pending_interview_answer, workflow_event,\n+    load_pending_interview, managed_run, paginate_items, parse_run_id_path, parse_stage_id_path,\n+    reject_if_archived, resolve_interp_string, submit_pending_interview_answer, workflow_event,\n };\n use crate::error::ApiError;\n use crate::principal_middleware::{\n-    RequireCommandLog, RequireRunScoped, RequireRunScopedOrRunTools, RequiredRunToolActor,\n-    RequiredUser,\n+    RequireCommandLog, RequireRunScoped, RequireRunScopedOrRunTools, RequireRunStageScoped,\n+    RequiredRunToolActor, RequiredUser,\n };\n use crate::run_files::{list_run_commits, list_run_files};\n use crate::run_manifest;\n@@ -73,6 +74,10 @@ pub(super) fn routes() -> Router<Arc<AppState>> {\n             \"/runs/{id}/stages/{stageId}/logs/output\",\n             get(get_run_stage_command_log),\n         )\n+        .route(\n+            \"/runs/{id}/stages/{stageId}/context-window\",\n+            get(get_run_stage_context_window),\n+        )\n         .route(\"/runs/{id}/settings\", get(get_run_settings))\n         .route(\"/runs/{id}/files\", get(list_run_files))\n         .route(\"/runs/{id}/commits\", get(list_run_commits))\n@@ -1015,6 +1020,70 @@ async fn get_run_logs(\n     }\n }\n \n+async fn get_run_stage_context_window(\n+    RequireRunStageScoped(id, raw_stage_id): RequireRunStageScoped,\n+    State(state): State<Arc<AppState>>,\n+) -> Response {\n+    let stage_id = match parse_stage_id_path(&raw_stage_id) {\n+        Ok(stage_id) => stage_id,\n+        Err(response) => return response,\n+    };\n+    let cached = match state.store.get_cached_run(&id).await {\n+        Ok(Some(cached)) => cached,\n+        Ok(None) => return ApiError::not_found(\"Run not found.\").into_response(),\n+        Err(err) => {\n+            return ApiError::new(StatusCode::INTERNAL_SERVER_ERROR, err.to_string())\n+                .into_response();\n+        }\n+    };\n+    let Some(stage) = cached.projection.stage(&stage_id) else {\n+        return ApiError::not_found(\"Stage not found.\").into_response();\n+    };\n+\n+    if !is_agent_context_window_stage(stage) {\n+        return Json(StageContextWindow::unavailable(\n+            stage_id,\n+            StageContextWindowUnavailableReason::NotAgentStage,\n+            \"Context-window data is only available for agent stages.\",\n+        ))\n+        .into_response();\n+    }\n+\n+    let Some(snapshot) = stage.context_window.as_ref() else {\n+        return Json(StageContextWindow::unavailable(\n+            stage_id,\n+            StageContextWindowUnavailableReason::NotObserved,\n+            \"No context-window snapshot has been observed for this stage.\",\n+        ))\n+        .into_response();\n+    };\n+\n+    let mut response = StageContextWindow::available(stage_id, snapshot);\n+    if !is_live_stage(stage.state) {\n+        response.staleness = StageContextWindowStaleness::Stored;\n+    }\n+    Json(response).into_response()\n+}\n+\n+fn is_agent_context_window_stage(stage: &StageProjection) -> bool {\n+    if stage.context_window.is_some() {\n+        return true;\n+    }\n+    if stage.handler == Some(StageHandler::Agent) {\n+        return true;\n+    }\n+    stage.provider_used.as_ref().is_some_and(|usage| {\n+        usage.mode == StageModelUsage::MODE_AGENT || usage.mode == StageModelUsage::MODE_ACP\n+    })\n+}\n+\n+fn is_live_stage(state: StageState) -> bool {\n+    matches!(\n+        state,\n+        StageState::Pending | StageState::Running | StageState::Retrying\n+    )\n+}\n+\n async fn get_run_stage_command_log(\n     RequireCommandLog(id, stage_id): RequireCommandLog,\n     State(state): State<Arc<AppState>>,\ndiff --git a/lib/crates/fabro-server/src/server/handler/sessions.rs b/lib/crates/fabro-server/src/server/handler/sessions.rs\nindex eaaac824e..f9c5d11b6 100644\n--- a/lib/crates/fabro-server/src/server/handler/sessions.rs\n+++ b/lib/crates/fabro-server/src/server/handler/sessions.rs\n@@ -1502,7 +1502,7 @@ mod tests {\n     use std::sync::atomic::{AtomicUsize, Ordering};\n \n     use fabro_agent::config::ToolAccess;\n-    use fabro_agent::tool_registry::{RegisteredTool, ToolContext, ToolRegistry};\n+    use fabro_agent::tool_registry::{RegisteredTool, ToolContext, ToolRegistry, ToolSource};\n     use fabro_llm::types::{ToolCall, ToolDefinition};\n \n     use super::*;\n@@ -1517,6 +1517,7 @@ mod tests {\n             executor:   Arc::new(|_args, _ctx: ToolContext| {\n                 Box::pin(async { Ok(\"ok\".to_string()) })\n             }),\n+            source:     ToolSource::Native,\n         }\n     }\n \n@@ -1785,6 +1786,7 @@ mod tests {\n                         Ok(\"executed\".to_string())\n                     })\n                 }),\n+                source:     ToolSource::Native,\n             });\n         }\n         let config = SessionOptions {\ndiff --git a/lib/crates/fabro-server/src/server/tests.rs b/lib/crates/fabro-server/src/server/tests.rs\nindex 2861a6bec..19bbfca35 100644\n--- a/lib/crates/fabro-server/src/server/tests.rs\n+++ b/lib/crates/fabro-server/src/server/tests.rs\n@@ -21,7 +21,10 @@ use fabro_types::settings::ServerAuthMethod;\n use fabro_types::{\n     AgentBackend, AttrValue, AuthMethod, CommandTermination, FailureCategory, FailureDetail, Graph,\n     InterviewQuestionRecord, Node, Outcome, QuestionType, RunBlobId, RunId, RunSpec,\n-    SandboxProvider, StageModelUsage, SuccessReason, SystemActorKind, WorkflowSettings, fixtures,\n+    SandboxProvider, StageContextWindowBreakdownProjection, StageContextWindowCategory,\n+    StageContextWindowCountMethod, StageContextWindowProjection, StageContextWindowStaleness,\n+    StageContextWindowWarning, StageModelUsage, StageTiming, SuccessReason, SystemActorKind,\n+    WorkflowSettings, fixtures,\n };\n use fabro_util::check_report::CheckStatus;\n use httpmock::Method::{GET, POST};\n@@ -2947,6 +2950,106 @@ async fn create_durable_run_with_events(\n     }\n }\n \n+fn stage_started_event(node_id: &str, handler_type: &str) -> workflow_event::Event {\n+    workflow_event::Event::StageStarted {\n+        node_id:      node_id.to_string(),\n+        name:         node_id.to_string(),\n+        index:        1,\n+        handler_type: handler_type.to_string(),\n+        attempt:      1,\n+        max_attempts: 1,\n+    }\n+}\n+\n+fn command_started_event(node_id: &str) -> workflow_event::Event {\n+    workflow_event::Event::CommandStarted {\n+        node_id:    node_id.to_string(),\n+        script:     \"echo ok\".to_string(),\n+        command:    \"echo ok\".to_string(),\n+        language:   \"shell\".to_string(),\n+        timeout_ms: None,\n+    }\n+}\n+\n+fn agent_session_activated_event(node_id: &str, visit: u32) -> workflow_event::Event {\n+    workflow_event::Event::AgentSessionActivated {\n+        node_id: node_id.to_string(),\n+        visit,\n+        session_id: \"session-1\".to_string(),\n+        thread_id: None,\n+        provider: Some(\"openai\".to_string()),\n+        model: Some(\"gpt-5.4\".to_string()),\n+        reasoning_effort: None,\n+        speed: None,\n+        capabilities: Vec::new(),\n+    }\n+}\n+\n+fn stage_completed_event(node_id: &str) -> workflow_event::Event {\n+    workflow_event::Event::StageCompleted {\n+        node_id: node_id.to_string(),\n+        name: node_id.to_string(),\n+        index: 1,\n+        timing: StageTiming::wall_only(42),\n+        status: \"succeeded\".to_string(),\n+        preferred_label: None,\n+        suggested_next_ids: Vec::new(),\n+        billing: None,\n+        failure: None,\n+        notes: None,\n+        files_touched: Vec::new(),\n+        context_updates: None,\n+        jump_to_node: None,\n+        context_values: None,\n+        node_visits: None,\n+        loop_failure_signatures: None,\n+        restart_failure_signatures: None,\n+        response: None,\n+        attempt: 1,\n+        max_attempts: 1,\n+    }\n+}\n+\n+fn context_window_event(\n+    stage: &str,\n+    visit: u32,\n+    snapshot: StageContextWindowProjection,\n+) -> workflow_event::Event {\n+    workflow_event::Event::Agent {\n+        stage: stage.to_string(),\n+        visit,\n+        event: fabro_agent::AgentEvent::ContextWindowSnapshot(snapshot),\n+        session_id: Some(\"session-1\".to_string()),\n+        parent_session_id: None,\n+        tool_call_id: None,\n+    }\n+}\n+\n+fn context_window_snapshot(\n+    input_tokens: u64,\n+    warnings: Vec<StageContextWindowWarning>,\n+) -> StageContextWindowProjection {\n+    StageContextWindowProjection {\n+        provider: \"openai\".to_string(),\n+        model: \"gpt-5.4\".to_string(),\n+        context_window_tokens: 400_000,\n+        input_tokens,\n+        usage_percent: input_tokens as f64 * 100.0 / 400_000.0,\n+        count_method: StageContextWindowCountMethod::ProviderApiScaledBreakdown,\n+        staleness: StageContextWindowStaleness::Live,\n+        generated_at: Utc::now(),\n+        event_seq: None,\n+        breakdown: vec![StageContextWindowBreakdownProjection {\n+            category:      StageContextWindowCategory::Conversation,\n+            label:         \"Conversation\".to_string(),\n+            tokens:        input_tokens,\n+            usage_percent: input_tokens as f64 * 100.0 / 400_000.0,\n+            source:        \"scaled_local_estimate\".to_string(),\n+        }],\n+        warnings,\n+    }\n+}\n+\n async fn append_default_run_created(run_store: &fabro_store::RunDatabase, run_id: RunId) {\n     workflow_event::append_event(run_store, &run_id, &workflow_event::Event::RunCreated {\n         run_id,\n@@ -6273,6 +6376,244 @@ async fn get_run_stage_command_log_returns_not_found_for_missing_stage() {\n     assert_status!(response, StatusCode::NOT_FOUND).await;\n }\n \n+#[tokio::test]\n+async fn get_run_stage_context_window_returns_not_found_for_missing_run() {\n+    let app = crate::test_support::build_test_router(test_app_state_with_isolated_storage());\n+    let run_id = RunId::new();\n+\n+    let response = app\n+        .oneshot(\n+            Request::builder()\n+                .method(\"GET\")\n+                .uri(api(&format!(\n+                    \"/runs/{run_id}/stages/agent@1/context-window\"\n+                )))\n+                .body(Body::empty())\n+                .unwrap(),\n+        )\n+        .await\n+        .unwrap();\n+\n+    assert_status!(response, StatusCode::NOT_FOUND).await;\n+}\n+\n+#[tokio::test]\n+async fn get_run_stage_context_window_returns_not_found_for_missing_stage() {\n+    let state = test_app_state_with_isolated_storage();\n+    let app = crate::test_support::build_test_router(Arc::clone(&state));\n+    let run_id = RunId::new();\n+    create_durable_run_with_events(&state, run_id, &[workflow_event::Event::RunSubmitted {\n+        definition_blob: None,\n+    }])\n+    .await;\n+\n+    let response = app\n+        .oneshot(\n+            Request::builder()\n+                .method(\"GET\")\n+                .uri(api(&format!(\n+                    \"/runs/{run_id}/stages/missing@1/context-window\"\n+                )))\n+                .body(Body::empty())\n+                .unwrap(),\n+        )\n+        .await\n+        .unwrap();\n+\n+    assert_status!(response, StatusCode::NOT_FOUND).await;\n+}\n+\n+#[tokio::test]\n+async fn get_run_stage_context_window_returns_unavailable_for_non_agent_stage() {\n+    let state = test_app_state_with_isolated_storage();\n+    let app = crate::test_support::build_test_router(Arc::clone(&state));\n+    let run_id = RunId::new();\n+    create_durable_run_with_events(&state, run_id, &[\n+        workflow_event::Event::RunSubmitted {\n+            definition_blob: None,\n+        },\n+        stage_started_event(\"script_node\", \"command\"),\n+        command_started_event(\"script_node\"),\n+    ])\n+    .await;\n+\n+    let body = response_json!(\n+        app.oneshot(\n+            Request::builder()\n+                .method(\"GET\")\n+                .uri(api(&format!(\n+                    \"/runs/{run_id}/stages/script_node@1/context-window\"\n+                )))\n+                .body(Body::empty())\n+                .unwrap(),\n+        )\n+        .await\n+        .unwrap(),\n+        StatusCode::OK\n+    )\n+    .await;\n+\n+    assert_eq!(body[\"available\"], false);\n+    assert_eq!(body[\"unavailable_reason\"], \"not_agent_stage\");\n+    assert_eq!(body[\"breakdown\"], json!([]));\n+    assert_eq!(body[\"staleness\"], \"unavailable\");\n+}\n+\n+#[tokio::test]\n+async fn get_run_stage_context_window_returns_not_observed_for_agent_stage_without_snapshot() {\n+    let state = test_app_state_with_isolated_storage();\n+    let app = crate::test_support::build_test_router(Arc::clone(&state));\n+    let run_id = RunId::new();\n+    create_durable_run_with_events(&state, run_id, &[\n+        workflow_event::Event::RunSubmitted {\n+            definition_blob: None,\n+        },\n+        agent_session_activated_event(\"agent_node\", 1),\n+    ])\n+    .await;\n+\n+    let body = response_json!(\n+        app.oneshot(\n+            Request::builder()\n+                .method(\"GET\")\n+                .uri(api(&format!(\n+                    \"/runs/{run_id}/stages/agent_node@1/context-window\"\n+                )))\n+                .body(Body::empty())\n+                .unwrap(),\n+        )\n+        .await\n+        .unwrap(),\n+        StatusCode::OK\n+    )\n+    .await;\n+\n+    assert_eq!(body[\"available\"], false);\n+    assert_eq!(body[\"unavailable_reason\"], \"not_observed\");\n+    assert_eq!(body[\"input_tokens\"], serde_json::Value::Null);\n+    assert!(!body[\"warnings\"].as_array().unwrap().is_empty());\n+}\n+\n+#[tokio::test]\n+async fn get_run_stage_context_window_returns_live_projected_snapshot() {\n+    let state = test_app_state_with_isolated_storage();\n+    let app = crate::test_support::build_test_router(Arc::clone(&state));\n+    let run_id = RunId::new();\n+    create_durable_run_with_events(&state, run_id, &[\n+        workflow_event::Event::RunSubmitted {\n+            definition_blob: None,\n+        },\n+        stage_started_event(\"agent_node\", \"agent\"),\n+        context_window_event(\n+            \"agent_node\",\n+            1,\n+            context_window_snapshot(123_456, Vec::new()),\n+        ),\n+    ])\n+    .await;\n+\n+    let body = response_json!(\n+        app.oneshot(\n+            Request::builder()\n+                .method(\"GET\")\n+                .uri(api(&format!(\n+                    \"/runs/{run_id}/stages/agent_node@1/context-window\"\n+                )))\n+                .body(Body::empty())\n+                .unwrap(),\n+        )\n+        .await\n+        .unwrap(),\n+        StatusCode::OK\n+    )\n+    .await;\n+\n+    assert_eq!(body[\"stage_id\"], \"agent_node@1\");\n+    assert_eq!(body[\"available\"], true);\n+    assert_eq!(body[\"provider\"], \"openai\");\n+    assert_eq!(body[\"count_method\"], \"provider_api_scaled_breakdown\");\n+    assert_eq!(body[\"staleness\"], \"live\");\n+    assert_eq!(body[\"input_tokens\"], 123_456);\n+    assert_eq!(body[\"breakdown\"][0][\"category\"], \"conversation\");\n+}\n+\n+#[tokio::test]\n+async fn get_run_stage_context_window_marks_completed_stage_snapshot_stored() {\n+    let state = test_app_state_with_isolated_storage();\n+    let app = crate::test_support::build_test_router(Arc::clone(&state));\n+    let run_id = RunId::new();\n+    create_durable_run_with_events(&state, run_id, &[\n+        workflow_event::Event::RunSubmitted {\n+            definition_blob: None,\n+        },\n+        stage_started_event(\"agent_node\", \"agent\"),\n+        context_window_event(\"agent_node\", 1, context_window_snapshot(100, Vec::new())),\n+        stage_completed_event(\"agent_node\"),\n+    ])\n+    .await;\n+\n+    let body = response_json!(\n+        app.oneshot(\n+            Request::builder()\n+                .method(\"GET\")\n+                .uri(api(&format!(\n+                    \"/runs/{run_id}/stages/agent_node@1/context-window\"\n+                )))\n+                .body(Body::empty())\n+                .unwrap(),\n+        )\n+        .await\n+        .unwrap(),\n+        StatusCode::OK\n+    )\n+    .await;\n+\n+    assert_eq!(body[\"available\"], true);\n+    assert_eq!(body[\"staleness\"], \"stored\");\n+    assert_eq!(body[\"input_tokens\"], 100);\n+}\n+\n+#[tokio::test]\n+async fn get_run_stage_context_window_returns_projected_warnings() {\n+    let state = test_app_state_with_isolated_storage();\n+    let app = crate::test_support::build_test_router(Arc::clone(&state));\n+    let run_id = RunId::new();\n+    create_durable_run_with_events(&state, run_id, &[\n+        workflow_event::Event::RunSubmitted {\n+            definition_blob: None,\n+        },\n+        stage_started_event(\"agent_node\", \"agent\"),\n+        context_window_event(\n+            \"agent_node\",\n+            1,\n+            context_window_snapshot(100, vec![StageContextWindowWarning {\n+                code:    \"provider_token_count_failed\".to_string(),\n+                message: \"provider input token counting failed; returned local estimate\"\n+                    .to_string(),\n+            }]),\n+        ),\n+    ])\n+    .await;\n+\n+    let body = response_json!(\n+        app.oneshot(\n+            Request::builder()\n+                .method(\"GET\")\n+                .uri(api(&format!(\n+                    \"/runs/{run_id}/stages/agent_node@1/context-window\"\n+                )))\n+                .body(Body::empty())\n+                .unwrap(),\n+        )\n+        .await\n+        .unwrap(),\n+        StatusCode::OK\n+    )\n+    .await;\n+\n+    assert_eq!(body[\"warnings\"][0][\"code\"], \"provider_token_count_failed\");\n+}\n+\n #[tokio::test]\n async fn get_run_pull_request_returns_live_detail_from_github() {\n     let github = MockServer::start();\ndiff --git a/lib/crates/fabro-store/src/run_state.rs b/lib/crates/fabro-store/src/run_state.rs\nindex f30e10598..9cd2a3716 100644\n--- a/lib/crates/fabro-store/src/run_state.rs\n+++ b/lib/crates/fabro-store/src/run_state.rs\n@@ -585,6 +585,15 @@ impl RunProjectionReducer for RunProjection {\n                     },\n                 });\n             }\n+            EventBody::AgentContextWindowSnapshot(props) => {\n+                let Some(stage) = stage_at_stored_or_visit(self, stored, props.visit, event.seq)\n+                else {\n+                    return Ok(());\n+                };\n+                let mut snapshot = props.snapshot.clone();\n+                snapshot.event_seq = Some(event.seq);\n+                stage.context_window = Some(snapshot);\n+            }\n             _ => {}\n         }\n \n@@ -1093,22 +1102,24 @@ mod tests {\n     use fabro_types::run_event::run::RunFailedProps;\n     use fabro_types::run_event::{\n         AgentAcpCancelledProps, AgentAcpCompletedProps, AgentAcpStartedProps,\n-        AgentAcpTimedOutProps, AgentMcpFailedProps, AgentMcpReadyProps, AgentMcpToolSummary,\n-        AgentMessageProps, AgentSessionActivatedProps, AgentSessionEndedProps,\n-        AgentSessionStartedProps, AgentSkillActivatedProps, AgentSkillActivationSource,\n-        AgentSkillSummary, AgentSkillsDiscoveredProps, AgentSubClosedProps, AgentSubCompletedProps,\n-        AgentSubFailedProps, AgentSubSpawnedProps, CheckpointCompletedProps,\n-        InterviewCompletedProps, InterviewOption, InterviewStartedProps, RunCompletedProps,\n-        RunControlEffectProps, StageCompletedProps, StageFailedProps, StagePromptProps,\n-        StageRetryingProps, StageStartedProps,\n+        AgentAcpTimedOutProps, AgentContextWindowSnapshotProps, AgentMcpFailedProps,\n+        AgentMcpReadyProps, AgentMcpToolSummary, AgentMessageProps, AgentSessionActivatedProps,\n+        AgentSessionEndedProps, AgentSessionStartedProps, AgentSkillActivatedProps,\n+        AgentSkillActivationSource, AgentSkillSummary, AgentSkillsDiscoveredProps,\n+        AgentSubClosedProps, AgentSubCompletedProps, AgentSubFailedProps, AgentSubSpawnedProps,\n+        CheckpointCompletedProps, InterviewCompletedProps, InterviewOption, InterviewStartedProps,\n+        RunCompletedProps, RunControlEffectProps, StageCompletedProps, StageFailedProps,\n+        StagePromptProps, StageRetryingProps, StageStartedProps,\n     };\n     use fabro_types::{\n         AgentBackend, BilledModelUsage, BilledTokenCounts, BlockedReason, Checkpoint,\n         CheckpointRecord, CommandTermination, EventBody, FailureCategory, FailureDetail,\n         FailureReason, Graph, McpServerStatus, Outcome, PendingReason, PullRequestLink,\n         QuestionType, ReasoningEffort, RunApprovalState, RunBlobId, RunControlAction, RunDiff,\n-        RunEvent, RunSize, RunSpec, RunStatus, Speed, StageModelUsage, StageOutcome, StageState,\n-        SubAgentStatus, SuccessReason, WorkflowSettings, first_event_seq, fixtures,\n+        RunEvent, RunSize, RunSpec, RunStatus, Speed, StageContextWindowBreakdownProjection,\n+        StageContextWindowCategory, StageContextWindowCountMethod, StageContextWindowProjection,\n+        StageContextWindowStaleness, StageContextWindowWarning, StageModelUsage, StageOutcome,\n+        StageState, SubAgentStatus, SuccessReason, WorkflowSettings, first_event_seq, fixtures,\n     };\n     use serde_json::json;\n \n@@ -4108,5 +4119,88 @@ mod tests {\n                 error: \"missing token\".to_string(),\n             });\n         }\n+\n+        #[test]\n+        fn context_window_snapshots_replace_latest_for_matching_stage() {\n+            let mut state = initialized_projection();\n+            let stage_id = stage_id();\n+            let first = context_window_snapshot(10);\n+            let second = context_window_snapshot(20);\n+\n+            state\n+                .apply_event(&test_stage_event(\n+                    7,\n+                    EventBody::AgentContextWindowSnapshot(AgentContextWindowSnapshotProps {\n+                        stage_id: stage_id.clone(),\n+                        visit:    1,\n+                        snapshot: first,\n+                    }),\n+                    stage_id.clone(),\n+                ))\n+                .unwrap();\n+            state\n+                .apply_event(&test_stage_event(\n+                    8,\n+                    EventBody::AgentContextWindowSnapshot(AgentContextWindowSnapshotProps {\n+                        stage_id: stage_id.clone(),\n+                        visit:    1,\n+                        snapshot: second,\n+                    }),\n+                    stage_id.clone(),\n+                ))\n+                .unwrap();\n+\n+            let stage = state.stage(&stage_id).unwrap();\n+            let snapshot = stage.context_window.as_ref().unwrap();\n+            assert_eq!(snapshot.input_tokens, 20);\n+            assert_eq!(snapshot.event_seq, Some(8));\n+        }\n+\n+        #[test]\n+        fn context_window_snapshot_does_not_update_other_stage() {\n+            let mut state = initialized_projection();\n+            let target = stage_id();\n+            let other = StageId::new(\"review\", 1);\n+\n+            state\n+                .apply_event(&test_stage_event(\n+                    7,\n+                    EventBody::AgentContextWindowSnapshot(AgentContextWindowSnapshotProps {\n+                        stage_id: target.clone(),\n+                        visit:    1,\n+                        snapshot: context_window_snapshot(10),\n+                    }),\n+                    target.clone(),\n+                ))\n+                .unwrap();\n+\n+            assert!(state.stage(&target).unwrap().context_window.is_some());\n+            assert!(state.stage(&other).is_none());\n+        }\n+\n+        fn context_window_snapshot(input_tokens: u64) -> StageContextWindowProjection {\n+            StageContextWindowProjection {\n+                provider: \"openai\".to_string(),\n+                model: \"gpt-5.4\".to_string(),\n+                context_window_tokens: 400_000,\n+                input_tokens,\n+                usage_percent: input_tokens as f64 * 100.0 / 400_000.0,\n+                count_method: StageContextWindowCountMethod::LocalEstimate,\n+                staleness: StageContextWindowStaleness::Live,\n+                generated_at: Utc::now(),\n+                event_seq: None,\n+                breakdown: vec![StageContextWindowBreakdownProjection {\n+                    category:      StageContextWindowCategory::Conversation,\n+                    label:         \"Conversation\".to_string(),\n+                    tokens:        input_tokens,\n+                    usage_percent: input_tokens as f64 * 100.0 / 400_000.0,\n+                    source:        \"local_estimate\".to_string(),\n+                }],\n+                warnings: vec![StageContextWindowWarning {\n+                    code:    \"local_token_estimate\".to_string(),\n+                    message: \"input token count is a local estimate\".to_string(),\n+                }],\n+            }\n+        }\n     }\n }\ndiff --git a/lib/crates/fabro-types/src/lib.rs b/lib/crates/fabro-types/src/lib.rs\nindex 3d2b64954..9f3104e6d 100644\n--- a/lib/crates/fabro-types/src/lib.rs\n+++ b/lib/crates/fabro-types/src/lib.rs\n@@ -104,8 +104,11 @@ pub use run_failure::RunFailure;\n pub use run_id::{RunId, fixtures};\n pub use run_projection::{\n     ActivatedSkill, CheckpointRecord, McpServerProjection, McpServerStatus, PendingInterviewRecord,\n-    RunProjection, SkillsProjection, StageModelUsage, StageProjection, SubAgentProjection,\n-    SubAgentStatus, first_event_seq,\n+    RunProjection, SkillsProjection, StageContextWindow, StageContextWindowBreakdownItem,\n+    StageContextWindowBreakdownProjection, StageContextWindowCategory,\n+    StageContextWindowCountMethod, StageContextWindowProjection, StageContextWindowStaleness,\n+    StageContextWindowUnavailableReason, StageContextWindowWarning, StageModelUsage,\n+    StageProjection, SubAgentProjection, SubAgentStatus, first_event_seq,\n };\n pub use run_sandbox::{RunSandbox, RunSandboxRuntime};\n pub use run_summary::{\ndiff --git a/lib/crates/fabro-types/src/run_event/agent.rs b/lib/crates/fabro-types/src/run_event/agent.rs\nindex cbf865496..81dbe0cd9 100644\n--- a/lib/crates/fabro-types/src/run_event/agent.rs\n+++ b/lib/crates/fabro-types/src/run_event/agent.rs\n@@ -4,7 +4,10 @@ use serde_json::Value;\n \n use super::BilledTokenCounts;\n use crate::transcript::{ToolCall, ToolResult, TranscriptMessage};\n-use crate::{MessageId, ModelRef, PairId, PairMessageId, PairSystemMessageKind, TurnId};\n+use crate::{\n+    MessageId, ModelRef, PairId, PairMessageId, PairSystemMessageKind,\n+    StageContextWindowProjection, StageId, TurnId,\n+};\n \n #[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]\n pub struct AgentSessionStartedProps {\n@@ -213,6 +216,14 @@ pub struct AgentLlmRetryProps {\n     pub visit:      u32,\n }\n \n+#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]\n+pub struct AgentContextWindowSnapshotProps {\n+    pub stage_id: StageId,\n+    pub visit:    u32,\n+    #[serde(flatten)]\n+    pub snapshot: StageContextWindowProjection,\n+}\n+\n #[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]\n pub struct AgentSubSpawnedProps {\n     pub agent_id: String,\ndiff --git a/lib/crates/fabro-types/src/run_event/mod.rs b/lib/crates/fabro-types/src/run_event/mod.rs\nindex 34ceb861b..35e8e72b7 100644\n--- a/lib/crates/fabro-types/src/run_event/mod.rs\n+++ b/lib/crates/fabro-types/src/run_event/mod.rs\n@@ -238,6 +238,8 @@ pub enum EventBody {\n     AgentCompactionCompleted(AgentCompactionCompletedProps),\n     #[serde(rename = \"agent.llm.retry\")]\n     AgentLlmRetry(AgentLlmRetryProps),\n+    #[serde(rename = \"agent.context_window.snapshot\")]\n+    AgentContextWindowSnapshot(AgentContextWindowSnapshotProps),\n     #[serde(rename = \"agent.sub.spawned\")]\n     AgentSubSpawned(AgentSubSpawnedProps),\n     #[serde(rename = \"agent.sub.completed\")]\n@@ -518,6 +520,7 @@ impl EventBody {\n             Self::AgentCompactionStarted(_) => \"agent.compaction.started\",\n             Self::AgentCompactionCompleted(_) => \"agent.compaction.completed\",\n             Self::AgentLlmRetry(_) => \"agent.llm.retry\",\n+            Self::AgentContextWindowSnapshot(_) => \"agent.context_window.snapshot\",\n             Self::AgentSubSpawned(_) => \"agent.sub.spawned\",\n             Self::AgentSubCompleted(_) => \"agent.sub.completed\",\n             Self::AgentSubFailed(_) => \"agent.sub.failed\",\n@@ -699,6 +702,7 @@ fn is_known_event_name(event: &str) -> bool {\n             | \"agent.compaction.started\"\n             | \"agent.compaction.completed\"\n             | \"agent.llm.retry\"\n+            | \"agent.context_window.snapshot\"\n             | \"agent.sub.spawned\"\n             | \"agent.sub.completed\"\n             | \"agent.sub.failed\"\n@@ -2151,6 +2155,48 @@ mod tests {\n         assert_eq!(value[\"properties\"][\"source\"], \"tool\");\n     }\n \n+    #[test]\n+    fn agent_context_window_snapshot_serializes_with_canonical_name() {\n+        let body = EventBody::AgentContextWindowSnapshot(AgentContextWindowSnapshotProps {\n+            stage_id: crate::StageId::new(\"implement\", 1),\n+            visit:    1,\n+            snapshot: crate::StageContextWindowProjection {\n+                provider:              \"openai\".to_string(),\n+                model:                 \"gpt-5.4\".to_string(),\n+                context_window_tokens: 400_000,\n+                input_tokens:          123_456,\n+                usage_percent:         30.864,\n+                count_method:\n+                    crate::StageContextWindowCountMethod::ProviderApiScaledBreakdown,\n+                staleness:             crate::StageContextWindowStaleness::Live,\n+                generated_at:          DateTime::parse_from_rfc3339(\"2026-05-23T12:34:56Z\")\n+                    .unwrap()\n+                    .with_timezone(&Utc),\n+                event_seq:             None,\n+                breakdown:             vec![crate::StageContextWindowBreakdownProjection {\n+                    category:      crate::StageContextWindowCategory::SystemPrompt,\n+                    label:         \"System prompt\".to_string(),\n+                    tokens:        30_000,\n+                    usage_percent: 7.5,\n+                    source:        \"scaled_local_estimate\".to_string(),\n+                }],\n+                warnings:              vec![crate::StageContextWindowWarning {\n+                    code:    \"local_token_estimate\".to_string(),\n+                    message: \"input token count is a local estimate\".to_string(),\n+                }],\n+            },\n+        });\n+        let value = serde_json::to_value(&body).unwrap();\n+        assert_eq!(value[\"event\"], \"agent.context_window.snapshot\");\n+        assert_eq!(value[\"properties\"][\"stage_id\"], \"implement@1\");\n+        assert_eq!(\n+            value[\"properties\"][\"breakdown\"][0][\"category\"],\n+            \"system_prompt\"\n+        );\n+        let parsed: EventBody = serde_json::from_value(value).unwrap();\n+        assert_eq!(parsed.event_name(), \"agent.context_window.snapshot\");\n+    }\n+\n     #[test]\n     fn agent_mcp_ready_deserializes_legacy_payload_without_tools() {\n         let value = json!({\ndiff --git a/lib/crates/fabro-types/src/run_projection.rs b/lib/crates/fabro-types/src/run_projection.rs\nindex 1f0aa11d5..46c13a4b1 100644\n--- a/lib/crates/fabro-types/src/run_projection.rs\n+++ b/lib/crates/fabro-types/src/run_projection.rs\n@@ -4,6 +4,7 @@ use std::num::NonZeroU32;\n \n use chrono::{DateTime, Utc};\n use fabro_model::{ReasoningEffort, Speed};\n+use strum::{Display, EnumString, IntoStaticStr};\n \n use crate::run_event::{AgentSessionActivatedProps, StagePromptProps};\n use crate::{\n@@ -117,6 +118,210 @@ impl StageModelUsage {\n     }\n }\n \n+#[derive(\n+    Debug,\n+    Clone,\n+    Copy,\n+    PartialEq,\n+    Eq,\n+    PartialOrd,\n+    Ord,\n+    Hash,\n+    serde::Serialize,\n+    serde::Deserialize,\n+    Display,\n+    EnumString,\n+    IntoStaticStr,\n+)]\n+#[serde(rename_all = \"snake_case\")]\n+#[strum(serialize_all = \"snake_case\")]\n+pub enum StageContextWindowCategory {\n+    SystemPrompt,\n+    Tools,\n+    McpTools,\n+    Skills,\n+    Memory,\n+    Conversation,\n+    Other,\n+}\n+\n+#[derive(\n+    Debug,\n+    Clone,\n+    Copy,\n+    PartialEq,\n+    Eq,\n+    Hash,\n+    serde::Serialize,\n+    serde::Deserialize,\n+    Display,\n+    EnumString,\n+    IntoStaticStr,\n+)]\n+#[serde(rename_all = \"snake_case\")]\n+#[strum(serialize_all = \"snake_case\")]\n+pub enum StageContextWindowCountMethod {\n+    ProviderApiScaledBreakdown,\n+    ResponseUsageScaledBreakdown,\n+    LocalEstimate,\n+}\n+\n+#[derive(\n+    Debug,\n+    Clone,\n+    Copy,\n+    PartialEq,\n+    Eq,\n+    Hash,\n+    serde::Serialize,\n+    serde::Deserialize,\n+    Display,\n+    EnumString,\n+    IntoStaticStr,\n+)]\n+#[serde(rename_all = \"snake_case\")]\n+#[strum(serialize_all = \"snake_case\")]\n+pub enum StageContextWindowStaleness {\n+    Live,\n+    Stored,\n+    Unavailable,\n+}\n+\n+#[derive(\n+    Debug,\n+    Clone,\n+    Copy,\n+    PartialEq,\n+    Eq,\n+    Hash,\n+    serde::Serialize,\n+    serde::Deserialize,\n+    Display,\n+    EnumString,\n+    IntoStaticStr,\n+)]\n+#[serde(rename_all = \"snake_case\")]\n+#[strum(serialize_all = \"snake_case\")]\n+pub enum StageContextWindowUnavailableReason {\n+    NotAgentStage,\n+    NotObserved,\n+    ProviderUnconfigured,\n+}\n+\n+#[derive(Debug, Clone, PartialEq, serde::Serialize, serde::Deserialize)]\n+pub struct StageContextWindowWarning {\n+    pub code:    String,\n+    pub message: String,\n+}\n+\n+#[derive(Debug, Clone, PartialEq, serde::Serialize, serde::Deserialize)]\n+pub struct StageContextWindowBreakdownProjection {\n+    pub category:      StageContextWindowCategory,\n+    pub label:         String,\n+    pub tokens:        u64,\n+    pub usage_percent: f64,\n+    pub source:        String,\n+}\n+\n+pub type StageContextWindowBreakdownItem = StageContextWindowBreakdownProjection;\n+\n+#[derive(Debug, Clone, PartialEq, serde::Serialize, serde::Deserialize)]\n+pub struct StageContextWindowProjection {\n+    pub provider:              String,\n+    pub model:                 String,\n+    pub context_window_tokens: u64,\n+    pub input_tokens:          u64,\n+    pub usage_percent:         f64,\n+    pub count_method:          StageContextWindowCountMethod,\n+    pub staleness:             StageContextWindowStaleness,\n+    pub generated_at:          DateTime<Utc>,\n+    #[serde(default, skip_serializing_if = \"Option::is_none\")]\n+    pub event_seq:             Option<u32>,\n+    #[serde(default)]\n+    pub breakdown:             Vec<StageContextWindowBreakdownProjection>,\n+    #[serde(default)]\n+    pub warnings:              Vec<StageContextWindowWarning>,\n+}\n+\n+#[derive(Debug, Clone, PartialEq, serde::Serialize, serde::Deserialize)]\n+pub struct StageContextWindow {\n+    pub stage_id:              StageId,\n+    pub available:             bool,\n+    #[serde(default)]\n+    pub unavailable_reason:    Option<StageContextWindowUnavailableReason>,\n+    #[serde(default)]\n+    pub provider:              Option<String>,\n+    #[serde(default)]\n+    pub model:                 Option<String>,\n+    #[serde(default)]\n+    pub context_window_tokens: Option<u64>,\n+    #[serde(default)]\n+    pub input_tokens:          Option<u64>,\n+    #[serde(default)]\n+    pub usage_percent:         Option<f64>,\n+    #[serde(default)]\n+    pub count_method:          Option<StageContextWindowCountMethod>,\n+    pub staleness:             StageContextWindowStaleness,\n+    #[serde(default)]\n+    pub generated_at:          Option<DateTime<Utc>>,\n+    #[serde(default)]\n+    pub event_seq:             Option<u32>,\n+    #[serde(default)]\n+    pub breakdown:             Vec<StageContextWindowBreakdownProjection>,\n+    #[serde(default)]\n+    pub warnings:              Vec<StageContextWindowWarning>,\n+}\n+\n+impl StageContextWindow {\n+    #[must_use]\n+    pub fn available(stage_id: StageId, snapshot: &StageContextWindowProjection) -> Self {\n+        Self {\n+            stage_id,\n+            available: true,\n+            unavailable_reason: None,\n+            provider: Some(snapshot.provider.clone()),\n+            model: Some(snapshot.model.clone()),\n+            context_window_tokens: Some(snapshot.context_window_tokens),\n+            input_tokens: Some(snapshot.input_tokens),\n+            usage_percent: Some(snapshot.usage_percent),\n+            count_method: Some(snapshot.count_method),\n+            staleness: snapshot.staleness,\n+            generated_at: Some(snapshot.generated_at),\n+            event_seq: snapshot.event_seq,\n+            breakdown: snapshot.breakdown.clone(),\n+            warnings: snapshot.warnings.clone(),\n+        }\n+    }\n+\n+    #[must_use]\n+    pub fn unavailable(\n+        stage_id: StageId,\n+        reason: StageContextWindowUnavailableReason,\n+        warning: impl Into<String>,\n+    ) -> Self {\n+        let message = warning.into();\n+        Self {\n+            stage_id,\n+            available: false,\n+            unavailable_reason: Some(reason),\n+            provider: None,\n+            model: None,\n+            context_window_tokens: None,\n+            input_tokens: None,\n+            usage_percent: None,\n+            count_method: None,\n+            staleness: StageContextWindowStaleness::Unavailable,\n+            generated_at: None,\n+            event_seq: None,\n+            breakdown: Vec::new(),\n+            warnings: vec![StageContextWindowWarning {\n+                code: reason.to_string(),\n+                message,\n+            }],\n+        }\n+    }\n+}\n+\n #[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]\n pub struct StageProjection {\n     pub first_event_seq:   NonZeroU32,\n@@ -158,6 +363,8 @@ pub struct StageProjection {\n     pub skills:            SkillsProjection,\n     #[serde(default, skip_serializing_if = \"Vec::is_empty\")]\n     pub mcp_servers:       Vec<McpServerProjection>,\n+    #[serde(default, skip_serializing_if = \"Option::is_none\")]\n+    pub context_window:    Option<StageContextWindowProjection>,\n     pub state:             StageState,\n }\n \n@@ -233,6 +440,7 @@ impl StageProjection {\n             subagents: Vec::new(),\n             skills: SkillsProjection::default(),\n             mcp_servers: Vec::new(),\n+            context_window: None,\n             provider_used: None,\n             diff: None,\n             script_invocation: None,\ndiff --git a/lib/crates/fabro-workflow/src/event/convert.rs b/lib/crates/fabro-workflow/src/event/convert.rs\nindex 2deda7c35..84383d46a 100644\n--- a/lib/crates/fabro-workflow/src/event/convert.rs\n+++ b/lib/crates/fabro-workflow/src/event/convert.rs\n@@ -590,7 +590,12 @@ fn event_body_from_event(event: &Event) -> EventBody {\n             provider: provider.clone(),\n             billing:  billing.clone(),\n         }),\n-        Event::Agent { visit, event, .. } => match event {\n+        Event::Agent {\n+            stage,\n+            visit,\n+            event,\n+            ..\n+        } => match event {\n             AgentEvent::ProcessingEnd => {\n                 EventBody::AgentProcessingEnd(fabro_types::AgentProcessingEndProps {\n                     visit: *visit,\n@@ -706,6 +711,13 @@ fn event_body_from_event(event: &Event) -> EventBody {\n                 error:      serde_json::to_value(error).expect(\"serializable sdk error\"),\n                 visit:      *visit,\n             }),\n+            AgentEvent::ContextWindowSnapshot(snapshot) => EventBody::AgentContextWindowSnapshot(\n+                fabro_types::AgentContextWindowSnapshotProps {\n+                    stage_id: ::fabro_types::StageId::new(stage.clone(), *visit),\n+                    visit:    *visit,\n+                    snapshot: snapshot.clone(),\n+                },\n+            ),\n             AgentEvent::SubAgentSpawned {\n                 agent_id,\n                 depth,\ndiff --git a/lib/crates/fabro-workflow/src/event/names.rs b/lib/crates/fabro-workflow/src/event/names.rs\nindex c4d19d832..958f8fdeb 100644\n--- a/lib/crates/fabro-workflow/src/event/names.rs\n+++ b/lib/crates/fabro-workflow/src/event/names.rs\n@@ -86,6 +86,7 @@ pub fn event_name(event: &Event) -> &'static str {\n             AgentEvent::CompactionStarted { .. } => \"agent.compaction.started\",\n             AgentEvent::CompactionCompleted { .. } => \"agent.compaction.completed\",\n             AgentEvent::LlmRetry { .. } => \"agent.llm.retry\",\n+            AgentEvent::ContextWindowSnapshot(_) => \"agent.context_window.snapshot\",\n             AgentEvent::SubAgentSpawned { .. } => \"agent.sub.spawned\",\n             AgentEvent::SubAgentCompleted { .. } => \"agent.sub.completed\",\n             AgentEvent::SubAgentFailed { .. } => \"agent.sub.failed\",\ndiff --git a/lib/crates/fabro-workflow/src/handler/llm/api.rs b/lib/crates/fabro-workflow/src/handler/llm/api.rs\nindex 8da02640d..7c2a90ad6 100644\n--- a/lib/crates/fabro-workflow/src/handler/llm/api.rs\n+++ b/lib/crates/fabro-workflow/src/handler/llm/api.rs\n@@ -3,7 +3,7 @@ use std::sync::{Arc, Mutex};\n \n use async_trait::async_trait;\n use fabro_agent::subagent::{SessionFactory, SubAgentManager};\n-use fabro_agent::tool_registry::{RegisteredTool, ToolContext, ToolRegistry};\n+use fabro_agent::tool_registry::{RegisteredTool, ToolContext, ToolRegistry, ToolSource};\n use fabro_agent::{\n     AgentEvent, AgentProfile, AnthropicProfile, CompletionCoordinator, GeminiProfile,\n     Message as AgentMessage, OpenAiProfile, Sandbox, Session, SessionOptions, StaticEnvProvider,\n@@ -233,6 +233,7 @@ fn fabro_run_tool(\n                     .map_err(|err| err.to_string())\n             })\n         }),\n+        source:     ToolSource::Native,\n     }\n }\n \ndiff --git a/lib/packages/fabro-api-client/src/api/run-internals-api.ts b/lib/packages/fabro-api-client/src/api/run-internals-api.ts\nindex 2ef4dbd6a..e46c2805f 100644\n--- a/lib/packages/fabro-api-client/src/api/run-internals-api.ts\n+++ b/lib/packages/fabro-api-client/src/api/run-internals-api.ts\n@@ -44,6 +44,8 @@ import type { RunEventDetailResponse } from '../models';\n // @ts-ignore\n import type { RunProjection } from '../models';\n // @ts-ignore\n+import type { StageContextWindow } from '../models';\n+// @ts-ignore\n import type { WorkflowSettings } from '../models';\n // @ts-ignore\n import type { WriteBlobResponse } from '../models';\n@@ -287,6 +289,50 @@ export const RunInternalsApiAxiosParamCreator = function (configuration?: Config\n                 options: localVarRequestOptions,\n             };\n         },\n+        /**\n+         * Returns the latest best-effort model-visible context-window usage snapshot for an agent stage.\n+         * @summary Get Stage Context Window\n+         * @param {string} id Unique run identifier (ULID).\n+         * @param {string} stageId Identifier of a stage within a run\\&#39;s workflow graph, serialized as &#x60;node_id@visit&#x60;.\n+         * @param {*} [options] Override http request option.\n+         * @throws {RequiredError}\n+         */\n+        getRunStageContextWindow: async (id: string, stageId: string, options: RawAxiosRequestConfig = {}): Promise<RequestArgs> => {\n+            // verify required parameter 'id' is not null or undefined\n+            assertParamExists('getRunStageContextWindow', 'id', id)\n+            // verify required parameter 'stageId' is not null or undefined\n+            assertParamExists('getRunStageContextWindow', 'stageId', stageId)\n+            const localVarPath = `/api/v1/runs/{id}/stages/{stageId}/context-window`\n+                .replace(`{${\"id\"}}`, encodeURIComponent(String(id)))\n+                .replace(`{${\"stageId\"}}`, encodeURIComponent(String(stageId)));\n+            // use dummy base URL string because the URL constructor only accepts absolute URLs.\n+            const localVarUrlObj = new URL(localVarPath, DUMMY_BASE_URL);\n+            let baseOptions;\n+            if (configuration) {\n+                baseOptions = configuration.baseOptions;\n+            }\n+\n+            const localVarRequestOptions = { method: 'GET', ...baseOptions, ...options};\n+            const localVarHeaderParameter = {} as any;\n+            const localVarQueryParameter = {} as any;\n+\n+            // authentication SessionCookie required\n+\n+            // authentication BearerAuth required\n+            // http bearer authentication required\n+            await setBearerAuthToObject(localVarHeaderParameter, configuration)\n+\n+            localVarHeaderParameter['Accept'] = 'application/json';\n+\n+            setSearchParams(localVarUrlObj, localVarQueryParameter);\n+            let headersFromBaseOptions = baseOptions && baseOptions.headers ? baseOptions.headers : {};\n+            localVarRequestOptions.headers = {...localVarHeaderParameter, ...headersFromBaseOptions, ...options.headers};\n+\n+            return {\n+                url: toPathString(localVarUrlObj),\n+                options: localVarRequestOptions,\n+            };\n+        },\n         /**\n          * Returns the internal event-sourced run projection. This is not a stable public contract.\n          * @summary Get Run State\n@@ -934,6 +980,20 @@ export const RunInternalsApiFp = function(configuration?: Configuration) {\n             const localVarOperationServerBasePath = operationServerMap['RunInternalsApi.getRunStageCommandLog']?.[localVarOperationServerIndex]?.url;\n             return (axios, basePath) => createRequestFunction(localVarAxiosArgs, globalAxios, BASE_PATH, configuration)(axios, localVarOperationServerBasePath || basePath);\n         },\n+        /**\n+         * Returns the latest best-effort model-visible context-window usage snapshot for an agent stage.\n+         * @summary Get Stage Context Window\n+         * @param {string} id Unique run identifier (ULID).\n+         * @param {string} stageId Identifier of a stage within a run\\&#39;s workflow graph, serialized as &#x60;node_id@visit&#x60;.\n+         * @param {*} [options] Override http request option.\n+         * @throws {RequiredError}\n+         */\n+        async getRunStageContextWindow(id: string, stageId: string, options?: RawAxiosRequestConfig): Promise<(axios?: AxiosInstance, basePath?: string) => AxiosPromise<StageContextWindow>> {\n+            const localVarAxiosArgs = await localVarAxiosParamCreator.getRunStageContextWindow(id, stageId, options);\n+            const localVarOperationServerIndex = configuration?.serverIndex ?? 0;\n+            const localVarOperationServerBasePath = operationServerMap['RunInternalsApi.getRunStageContextWindow']?.[localVarOperationServerIndex]?.url;\n+            return (axios, basePath) => createRequestFunction(localVarAxiosArgs, globalAxios, BASE_PATH, configuration)(axios, localVarOperationServerBasePath || basePath);\n+        },\n         /**\n          * Returns the internal event-sourced run projection. This is not a stable public contract.\n          * @summary Get Run State\n@@ -1173,6 +1233,17 @@ export const RunInternalsApiFactory = function (configuration?: Configuration, b\n         getRunStageCommandLog(id: string, stageId: string, offset?: number, limit?: number, options?: RawAxiosRequestConfig): AxiosPromise<CommandLogResponse> {\n             return localVarFp.getRunStageCommandLog(id, stageId, offset, limit, options).then((request) => request(axios, basePath));\n         },\n+        /**\n+         * Returns the latest best-effort model-visible context-window usage snapshot for an agent stage.\n+         * @summary Get Stage Context Window\n+         * @param {string} id Unique run identifier (ULID).\n+         * @param {string} stageId Identifier of a stage within a run\\&#39;s workflow graph, serialized as &#x60;node_id@visit&#x60;.\n+         * @param {*} [options] Override http request option.\n+         * @throws {RequiredError}\n+         */\n+        getRunStageContextWindow(id: string, stageId: string, options?: RawAxiosRequestConfig): AxiosPromise<StageContextWindow> {\n+            return localVarFp.getRunStageContextWindow(id, stageId, options).then((request) => request(axios, basePath));\n+        },\n         /**\n          * Returns the internal event-sourced run projection. This is not a stable public contract.\n          * @summary Get Run State\n@@ -1379,6 +1450,18 @@ export class RunInternalsApi extends BaseAPI {\n         return RunInternalsApiFp(this.configuration).getRunStageCommandLog(id, stageId, offset, limit, options).then((request) => request(this.axios, this.basePath));\n     }\n \n+    /**\n+     * Returns the latest best-effort model-visible context-window usage snapshot for an agent stage.\n+     * @summary Get Stage Context Window\n+     * @param {string} id Unique run identifier (ULID).\n+     * @param {string} stageId Identifier of a stage within a run\\&#39;s workflow graph, serialized as &#x60;node_id@visit&#x60;.\n+     * @param {*} [options] Override http request option.\n+     * @throws {RequiredError}\n+     */\n+    public getRunStageContextWindow(id: string, stageId: string, options?: RawAxiosRequestConfig) {\n+        return RunInternalsApiFp(this.configuration).getRunStageContextWindow(id, stageId, options).then((request) => request(this.axios, this.basePath));\n+    }\n+\n     /**\n      * Returns the internal event-sourced run projection. This is not a stable public contract.\n      * @summary Get Run State\ndiff --git a/lib/packages/fabro-api-client/src/models/index.ts b/lib/packages/fabro-api-client/src/models/index.ts\nindex 4ea2bd295..513de50bc 100644\n--- a/lib/packages/fabro-api-client/src/models/index.ts\n+++ b/lib/packages/fabro-api-client/src/models/index.ts\n@@ -371,6 +371,14 @@ export * from './slack-integration-settings';\n export * from './ssh-access-request';\n export * from './ssh-access-response';\n export * from './stage-completion';\n+export * from './stage-context-window';\n+export * from './stage-context-window-breakdown-item';\n+export * from './stage-context-window-category';\n+export * from './stage-context-window-count-method';\n+export * from './stage-context-window-projection';\n+export * from './stage-context-window-staleness';\n+export * from './stage-context-window-unavailable-reason';\n+export * from './stage-context-window-warning';\n export * from './stage-handler';\n export * from './stage-model-usage';\n export * from './stage-outcome';\ndiff --git a/lib/packages/fabro-api-client/src/models/stage-context-window-breakdown-item.ts b/lib/packages/fabro-api-client/src/models/stage-context-window-breakdown-item.ts\nnew file mode 100644\nindex 000000000..0798a16d5\n--- /dev/null\n+++ b/lib/packages/fabro-api-client/src/models/stage-context-window-breakdown-item.ts\n@@ -0,0 +1,31 @@\n+/* tslint:disable */\n+/* eslint-disable */\n+/**\n+ * Fabro Run API\n+ * HTTP API for managing Fabro workflow run executions.\n+ *\n+ * The version of the OpenAPI document: 0.1.0\n+ *\n+ *\n+ * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n+ * https://openapi-generator.tech\n+ * Do not edit the class manually.\n+ */\n+\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowCategory } from './stage-context-window-category';\n+\n+/**\n+ * Token usage for one content category.\n+ */\n+export interface StageContextWindowBreakdownItem {\n+    'category': StageContextWindowCategory;\n+    'label': string;\n+    'tokens': number;\n+    'usage_percent': number;\n+    /**\n+     * Content-free source label for the category count.\n+     */\n+    'source': string;\n+}\ndiff --git a/lib/packages/fabro-api-client/src/models/stage-context-window-category.ts b/lib/packages/fabro-api-client/src/models/stage-context-window-category.ts\nnew file mode 100644\nindex 000000000..76db9bbb3\n--- /dev/null\n+++ b/lib/packages/fabro-api-client/src/models/stage-context-window-category.ts\n@@ -0,0 +1,28 @@\n+/* tslint:disable */\n+/* eslint-disable */\n+/**\n+ * Fabro Run API\n+ * HTTP API for managing Fabro workflow run executions.\n+ *\n+ * The version of the OpenAPI document: 0.1.0\n+ *\n+ *\n+ * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n+ * https://openapi-generator.tech\n+ * Do not edit the class manually.\n+ */\n+\n+/**\n+ * Category of model-visible input/context tokens.\n+ */\n+export const StageContextWindowCategory = {\n+    SYSTEM_PROMPT: 'system_prompt',\n+    TOOLS: 'tools',\n+    MCP_TOOLS: 'mcp_tools',\n+    SKILLS: 'skills',\n+    MEMORY: 'memory',\n+    CONVERSATION: 'conversation',\n+    OTHER: 'other'\n+} as const;\n+\n+export type StageContextWindowCategory = typeof StageContextWindowCategory[keyof typeof StageContextWindowCategory];\ndiff --git a/lib/packages/fabro-api-client/src/models/stage-context-window-count-method.ts b/lib/packages/fabro-api-client/src/models/stage-context-window-count-method.ts\nnew file mode 100644\nindex 000000000..c699d08dc\n--- /dev/null\n+++ b/lib/packages/fabro-api-client/src/models/stage-context-window-count-method.ts\n@@ -0,0 +1,24 @@\n+/* tslint:disable */\n+/* eslint-disable */\n+/**\n+ * Fabro Run API\n+ * HTTP API for managing Fabro workflow run executions.\n+ *\n+ * The version of the OpenAPI document: 0.1.0\n+ *\n+ *\n+ * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n+ * https://openapi-generator.tech\n+ * Do not edit the class manually.\n+ */\n+\n+/**\n+ * Method used to produce the context-window token total and breakdown.\n+ */\n+export const StageContextWindowCountMethod = {\n+    PROVIDER_API_SCALED_BREAKDOWN: 'provider_api_scaled_breakdown',\n+    RESPONSE_USAGE_SCALED_BREAKDOWN: 'response_usage_scaled_breakdown',\n+    LOCAL_ESTIMATE: 'local_estimate'\n+} as const;\n+\n+export type StageContextWindowCountMethod = typeof StageContextWindowCountMethod[keyof typeof StageContextWindowCountMethod];\ndiff --git a/lib/packages/fabro-api-client/src/models/stage-context-window-projection.ts b/lib/packages/fabro-api-client/src/models/stage-context-window-projection.ts\nnew file mode 100644\nindex 000000000..888e002cc\n--- /dev/null\n+++ b/lib/packages/fabro-api-client/src/models/stage-context-window-projection.ts\n@@ -0,0 +1,43 @@\n+/* tslint:disable */\n+/* eslint-disable */\n+/**\n+ * Fabro Run API\n+ * HTTP API for managing Fabro workflow run executions.\n+ *\n+ * The version of the OpenAPI document: 0.1.0\n+ *\n+ *\n+ * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n+ * https://openapi-generator.tech\n+ * Do not edit the class manually.\n+ */\n+\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowBreakdownItem } from './stage-context-window-breakdown-item';\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowCountMethod } from './stage-context-window-count-method';\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowStaleness } from './stage-context-window-staleness';\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowWarning } from './stage-context-window-warning';\n+\n+/**\n+ * Durable content-free context-window snapshot projected onto an agent stage.\n+ */\n+export interface StageContextWindowProjection {\n+    'provider': string;\n+    'model': string;\n+    'context_window_tokens': number;\n+    'input_tokens': number;\n+    'usage_percent': number;\n+    'count_method': StageContextWindowCountMethod;\n+    'staleness': StageContextWindowStaleness;\n+    'generated_at': string;\n+    'event_seq'?: number | null;\n+    'breakdown': Array<StageContextWindowBreakdownItem>;\n+    'warnings': Array<StageContextWindowWarning>;\n+}\ndiff --git a/lib/packages/fabro-api-client/src/models/stage-context-window-staleness.ts b/lib/packages/fabro-api-client/src/models/stage-context-window-staleness.ts\nnew file mode 100644\nindex 000000000..6c00de7a3\n--- /dev/null\n+++ b/lib/packages/fabro-api-client/src/models/stage-context-window-staleness.ts\n@@ -0,0 +1,24 @@\n+/* tslint:disable */\n+/* eslint-disable */\n+/**\n+ * Fabro Run API\n+ * HTTP API for managing Fabro workflow run executions.\n+ *\n+ * The version of the OpenAPI document: 0.1.0\n+ *\n+ *\n+ * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n+ * https://openapi-generator.tech\n+ * Do not edit the class manually.\n+ */\n+\n+/**\n+ * Freshness of the returned context-window data.\n+ */\n+export const StageContextWindowStaleness = {\n+    LIVE: 'live',\n+    STORED: 'stored',\n+    UNAVAILABLE: 'unavailable'\n+} as const;\n+\n+export type StageContextWindowStaleness = typeof StageContextWindowStaleness[keyof typeof StageContextWindowStaleness];\ndiff --git a/lib/packages/fabro-api-client/src/models/stage-context-window-unavailable-reason.ts b/lib/packages/fabro-api-client/src/models/stage-context-window-unavailable-reason.ts\nnew file mode 100644\nindex 000000000..218f03a9e\n--- /dev/null\n+++ b/lib/packages/fabro-api-client/src/models/stage-context-window-unavailable-reason.ts\n@@ -0,0 +1,24 @@\n+/* tslint:disable */\n+/* eslint-disable */\n+/**\n+ * Fabro Run API\n+ * HTTP API for managing Fabro workflow run executions.\n+ *\n+ * The version of the OpenAPI document: 0.1.0\n+ *\n+ *\n+ * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n+ * https://openapi-generator.tech\n+ * Do not edit the class manually.\n+ */\n+\n+/**\n+ * Why context-window data is unavailable for a known run stage.\n+ */\n+export const StageContextWindowUnavailableReason = {\n+    NOT_AGENT_STAGE: 'not_agent_stage',\n+    NOT_OBSERVED: 'not_observed',\n+    PROVIDER_UNCONFIGURED: 'provider_unconfigured'\n+} as const;\n+\n+export type StageContextWindowUnavailableReason = typeof StageContextWindowUnavailableReason[keyof typeof StageContextWindowUnavailableReason];\ndiff --git a/lib/packages/fabro-api-client/src/models/stage-context-window-warning.ts b/lib/packages/fabro-api-client/src/models/stage-context-window-warning.ts\nnew file mode 100644\nindex 000000000..8e60ec2bf\n--- /dev/null\n+++ b/lib/packages/fabro-api-client/src/models/stage-context-window-warning.ts\n@@ -0,0 +1,27 @@\n+/* tslint:disable */\n+/* eslint-disable */\n+/**\n+ * Fabro Run API\n+ * HTTP API for managing Fabro workflow run executions.\n+ *\n+ * The version of the OpenAPI document: 0.1.0\n+ *\n+ *\n+ * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n+ * https://openapi-generator.tech\n+ * Do not edit the class manually.\n+ */\n+\n+/**\n+ * Content-free warning about context-window count quality or attribution.\n+ */\n+export interface StageContextWindowWarning {\n+    /**\n+     * Stable warning code.\n+     */\n+    'code': string;\n+    /**\n+     * Human-readable warning that must not include prompt, memory, message, or tool-argument content.\n+     */\n+    'message': string;\n+}\ndiff --git a/lib/packages/fabro-api-client/src/models/stage-context-window.ts b/lib/packages/fabro-api-client/src/models/stage-context-window.ts\nnew file mode 100644\nindex 000000000..0d7905c50\n--- /dev/null\n+++ b/lib/packages/fabro-api-client/src/models/stage-context-window.ts\n@@ -0,0 +1,55 @@\n+/* tslint:disable */\n+/* eslint-disable */\n+/**\n+ * Fabro Run API\n+ * HTTP API for managing Fabro workflow run executions.\n+ *\n+ * The version of the OpenAPI document: 0.1.0\n+ *\n+ *\n+ * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n+ * https://openapi-generator.tech\n+ * Do not edit the class manually.\n+ */\n+\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowBreakdownItem } from './stage-context-window-breakdown-item';\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowCountMethod } from './stage-context-window-count-method';\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowStaleness } from './stage-context-window-staleness';\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowUnavailableReason } from './stage-context-window-unavailable-reason';\n+// May contain unused imports in some cases\n+// @ts-ignore\n+import type { StageContextWindowWarning } from './stage-context-window-warning';\n+\n+/**\n+ * Best-effort context-window usage for one agent stage.\n+ */\n+export interface StageContextWindow {\n+    /**\n+     * Stage ID in `node@visit` form.\n+     */\n+    'stage_id': string;\n+    /**\n+     * Whether context-window data is available for this known stage.\n+     */\n+    'available': boolean;\n+    'unavailable_reason': StageContextWindowUnavailableReason | null;\n+    'provider': string | null;\n+    'model': string | null;\n+    'context_window_tokens': number | null;\n+    'input_tokens': number | null;\n+    'usage_percent': number | null;\n+    'count_method': StageContextWindowCountMethod | null;\n+    'staleness': StageContextWindowStaleness;\n+    'generated_at': string | null;\n+    'event_seq': number | null;\n+    'breakdown': Array<StageContextWindowBreakdownItem>;\n+    'warnings': Array<StageContextWindowWarning>;\n+}\ndiff --git a/lib/packages/fabro-api-client/src/models/stage-projection.ts b/lib/packages/fabro-api-client/src/models/stage-projection.ts\nindex cdec8e984..b4a29b6ce 100644\n--- a/lib/packages/fabro-api-client/src/models/stage-projection.ts\n+++ b/lib/packages/fabro-api-client/src/models/stage-projection.ts\n@@ -33,6 +33,9 @@ import type { SkillsProjection } from './skills-projection';\n import type { StageCompletion } from './stage-completion';\n // May contain unused imports in some cases\n // @ts-ignore\n+import type { StageContextWindowProjection } from './stage-context-window-projection';\n+// May contain unused imports in some cases\n+// @ts-ignore\n import type { StageModelUsage } from './stage-model-usage';\n // May contain unused imports in some cases\n // @ts-ignore\n@@ -93,6 +96,10 @@ export interface StageProjection {\n      * MCP servers observed by this stage.\n      */\n     'mcp_servers'?: Array<McpServerProjection>;\n+    /**\n+     * Latest content-free context-window snapshot for this agent stage.\n+     */\n+    'context_window'?: StageContextWindowProjection | null;\n     /**\n      * Lifecycle state of the stage projection.\n      */\n",
        "summary": {
          "files_changed": 47,
          "additions": 2471,
          "deletions": 105
        }
      }
    },
    {
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          "response.implement": "Implemented the context-window breakdown endpoint end-to-end.\n\n### What changed\n\n- Added OpenAPI contract for:\n  - `GET /api/v1/runs/{id}/stages/{stageId}/context-window`\n  - `StageContextWindow*` schemas/enums/warnings/breakdown DTOs.\n- Added content-free projection types in `fabro-types` and wired them through `fabro-api` replacements/tests.\n- Added typed event:\n  - `agent.context_window.snapshot`\n- Added reducer support in `fabro-store` so latest snapshots replace prior stage snapshots.\n- Added agent-side context-window accounting:\n  - local category breakdown at request assembly time\n  - async provider token count via `Client::count_input_tokens(... PreferProvider)`\n  - provider/API scaled replacement snapshots\n  - response-usage scaled replacement snapshots\n  - content-safe request fingerprinting and close-token cancellation for count tasks\n- Added tool source metadata (`Native`, `Mcp`, `Skill`) so tool definitions split into native tools, MCP tools, and skills.\n- Exposed narrow token-estimation helpers from `fabro-llm`.\n- Added server endpoint behavior:\n  - 404 for missing run/stage\n  - `available: false` for non-agent stages and unobserved agent stages\n  - stored/latest projected snapshot response with warnings preserved\n- Added web query support:\n  - `queryKeys.runs.stageContextWindow(id, stageId)`\n  - `useRunStageContextWindow(runId, stageId)`\n  - SSE invalidation for context-window snapshots, stage lifecycle, steering/activity, and todo activity.\n- Updated TypeScript generated client files manually for the new endpoint/models because the generator could not run in this environment.\n\n### Validation run\n\nPassed:\n\n- `cargo build -q -p fabro-api`\n- `cargo check -q --workspace`\n- `cargo nextest run -p fabro-types -p fabro-store -p fabro-api --no-fail-fast`\n- `cargo nextest run -p fabro-agent --no-fail-fast`\n- `cargo nextest run -p fabro-workflow --no-fail-fast`\n- `cargo nextest run -p fabro-server get_run_stage_context_window --no-fail-fast`\n- `cargo +nightly-2026-04-14 fmt --check --all`\n- `cargo +nightly-2026-04-14 clippy --workspace --all-targets -- -D warnings`\n- `git diff --check`\n- `cd apps/fabro-web && bun test app/lib/query-keys.test.ts app/lib/run-events.test.tsx`\n- `cd apps/fabro-web && bun run typecheck`\n- `cd apps/fabro-web && bun test app/lib/run-actions.test.ts`\n\nNotes:\n\n- `cd lib/packages/fabro-api-client && bun run generate` failed because the environment lacks `java`; `bunx` latest also hit a Node 18 ESM issue. I manually updated the generated TypeScript client/model files to match the OpenAPI change.\n- Full `cd apps/fabro-web && bun test` still reports failures in `run-actions.test.ts` only when run as part of the entire suite; that file passes in isolation. This appears to be pre-existing/global test-state interference, not caused by the context-window changes.\n- Full `cargo nextest run -p fabro-server` previously surfaced unrelated graph-render subprocess failures; the new context-window endpoint tests pass directly.",
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          "response.simplify_opus": "All changes complete. Here's a summary of what was fixed.\n\n## Summary\n\nThree review agents identified findings across reuse, quality, and efficiency. I applied the highest-impact, clearly-correct fixes and skipped the ones that turned out to be wrong on closer inspection (e.g. cache-token math; the reviewer was mistaken — input_tokens excludes cache tokens for all three providers, so the original addition is correct).\n\n### Fixes applied\n\n1. **Web invalidation scope** (`apps/fabro-web/app/lib/run-events.ts` + tests): the context-window endpoint's projection only changes on `agent.context_window.snapshot` events, so `stageContextWindow` invalidation was removed from `STAGE_EVENTS`, `STEERING_EVENTS`, `STAGE_ACTIVITY_EVENTS`, and `TODO_EVENTS` branches. Saves an HTTP refetch on every agent activity event.\n\n2. **Reuse `StageState::is_terminal()`** (`lib/crates/fabro-server/src/server/handler/runs.rs`): replaced the local `is_live_stage` helper with `stage.state.is_terminal()` and dropped the unused `StageState` import.\n\n3. **Consolidate breakdown item type** (`lib/crates/fabro-types/src/run_projection.rs` + OpenAPI + generated TS): dropped the `StageContextWindowBreakdownItem` type alias by renaming `StageContextWindowBreakdownProjection` to `StageContextWindowBreakdownItem`. Per CLAUDE.md API DTO guidance.\n\n4. **Dropped redundant `label` and `source` fields** from breakdown items (OpenAPI, Rust projection, generated TS, agent estimator, all test fixtures). `label` was UI text in the API; `source` duplicated the outer `count_method`.\n\n5. **Simplified `scale_breakdown`** (`lib/crates/fabro-agent/src/context_window.rs`): replaced ~60-line largest-remainder algorithm with a simple proportional scale where the last bucket absorbs the rounding leftover. Acceptable for a best-effort estimate.\n\n6. **Consolidated `ToolRegistry` policy filters** (`lib/crates/fabro-agent/src/tool_registry.rs`): `definitions_for_policy` now delegates to `definitions_with_source_for_policy`, removing the duplicated filter logic.\n\n### Validation\n\n- `cargo +nightly-2026-04-14 clippy --workspace --all-targets -- -D warnings`: clean\n- `cargo +nightly-2026-04-14 fmt --check --all`: clean\n- `cargo nextest run -p fabro-agent -p fabro-api -p fabro-store -p fabro-server -p fabro-types`: 1674 passed, 3 failed (all pre-existing graphviz subprocess failures unrelated to this change, confirmed by running the same tests on `origin/main`).\n- `apps/fabro-web bun test app/lib/query-keys.test.ts app/lib/run-events.test.tsx`: 21/21 pass\n- `apps/fabro-web bun run typecheck`: clean\n\n### Findings deliberately skipped\n\n- **Drop spawned provider-API count**: per Decision 2 of the original plan, provider counting at request assembly is explicitly required. Removing it would deviate from the spec.\n- **Cache-token math (`input + cache_read + cache_write`)**: confirmed correct by reading each provider's adapter — OpenAI/Gemini explicitly `saturating_sub(cached_tokens)` from `input_tokens`, and Anthropic's `input_tokens` is documented as uncached. Sum is the right total context size.\n- **Per-message token cache (O(N²) concern)**: real but the estimator already short-circuits zero-token additions and only runs once per agent turn; deferring this since it would add cache invalidation complexity to a best-effort feature.\n- **Bigger DTO/projection unification (`StageContextWindow` flattens projection)**: legitimate but a larger refactor that risks the response shape; left in place since current shape is already covered by tests and the build.rs round-trip.\n- **Several lower-severity findings** (e.g. `BuiltRequest` wrapper, `close_token` field, `request_fingerprint` smell) are noise compared to the size of the diff; not worth the churn in a greenfield-priority pass that didn't elect to drop the provider count path itself.",
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          "graph.goal": "# Context Window Breakdown Endpoint Plan\n\nDate: 2026-05-23\n\n## Context\n\nAgent stage pages already have enough projected data for todos, subagents,\nskills, and MCP servers through `StageProjection` in\n`lib/crates/fabro-types/src/run_projection.rs` and the reducer in\n`lib/crates/fabro-store/src/run_state.rs`. Context-window usage is different:\nFabro emits context-window warnings and compaction events today, but it does not\nstore a category breakdown of the model-visible request context.\n\n`fabro-llm` already exposes the right counting primitive:\n`Client::count_input_tokens(request, InputTokenCountPreference::PreferProvider)`\nin `lib/crates/fabro-llm/src/client.rs`. Provider adapters can call native count\nendpoints for OpenAI, Anthropic, and Gemini, and the client already falls back\nto local estimates for fallback-eligible failures.\n\n## Goal\n\nAdd a best-effort context-window API for agent stages:\n\n```text\nGET /api/v1/runs/{id}/stages/{stageId}/context-window\n```\n\nThe endpoint should return the best context-window usage Fabro can produce with\nno caller-controlled count or accuracy parameters. It may call the configured\nLLM provider by default. If provider counting is not possible, it should degrade\nto a local estimate or the latest stored snapshot instead of making the sidebar\ntreat ordinary count gaps as hard errors.\n\n## Scope\n\nIn scope:\n\n- OpenAPI contract and generated Rust/TypeScript clients.\n- Content-free context-window DTOs in the run projection.\n- A typed agent event for latest context-window snapshots.\n- Server endpoint that combines live provider counting, local estimates, and\n  stored-snapshot fallback.\n- Web query key, hook, and SSE invalidation support so the future sidebar can\n  consume the endpoint.\n\nOut of scope:\n\n- Building the full new left sidebar UI.\n- Persisting raw prompt, memory, tool arguments, or message contents for later\n  token counting.\n- Adding user-visible count-mode or accuracy knobs.\n- Retrofitting exact historical context-window counts for older completed runs.\n\nBefore implementing, read:\n\n- `docs/internal/events-strategy.md`\n- `docs/internal/error-handling-strategy.md`\n- `docs/internal/testing-strategy.md`\n\n## API Contract\n\nAdd the route under the existing Run Internals tag in\n`docs/public/api-reference/fabro-api.yaml`:\n\n```text\nGET /runs/{id}/stages/{stageId}/context-window\n```\n\nProposed response shape:\n\n```json\n{\n  \"stage_id\": \"implement@1\",\n  \"available\": true,\n  \"unavailable_reason\": null,\n  \"provider\": \"openai\",\n  \"model\": \"gpt-5.4\",\n  \"context_window_tokens\": 400000,\n  \"input_tokens\": 123456,\n  \"usage_percent\": 30.86,\n  \"count_method\": \"provider_api_scaled_breakdown\",\n  \"staleness\": \"live\",\n  \"generated_at\": \"2026-05-23T12:34:56Z\",\n  \"event_seq\": 42,\n  \"breakdown\": [\n    {\n      \"category\": \"system_prompt\",\n      \"label\": \"System prompt\",\n      \"tokens\": 30000,\n      \"usage_percent\": 7.5,\n      \"source\": \"scaled_local_estimate\"\n    }\n  ],\n  \"warnings\": []\n}\n```\n\nRequired schemas:\n\n- `StageContextWindow`\n- `StageContextWindowBreakdownItem`\n- `StageContextWindowCategory`\n- `StageContextWindowCountMethod`\n- `StageContextWindowStaleness`\n- `StageContextWindowUnavailableReason`\n- `StageContextWindowWarning`\n\nEnums:\n\n```text\nStageContextWindowCategory:\n  system_prompt\n  tools\n  mcp_tools\n  skills\n  memory\n  conversation\n  other\n\nStageContextWindowCountMethod:\n  provider_api_scaled_breakdown\n  response_usage_scaled_breakdown\n  local_estimate\n\nStageContextWindowStaleness:\n  live\n  stored\n  unavailable\n\nStageContextWindowUnavailableReason:\n  not_agent_stage\n  not_observed\n  provider_unconfigured\n```\n\nUse `available: false` for a real run/stage where Fabro has no context-window\ndata yet. Missing runs and missing stages should still return 404. For\n`available: false`, return `breakdown: []`, `warnings` explaining the gap, and\nnullable token fields.\n\nDefine context-window usage as model-visible input/context tokens only:\n\n- Include prompt input, system/developer content, tool definitions, MCP tool\n  definitions, skills, memory files, conversation history, tool results, and\n  cache input tokens when response usage is the source.\n- Exclude output tokens and reasoning tokens.\n- Do not report cost/billing totals here. Existing billing APIs own billing.\n\n## Data Model\n\nAdd projection-only, content-free context-window types to\n`lib/crates/fabro-types/src/run_projection.rs`:\n\n- `StageContextWindowProjection`\n- `StageContextWindowBreakdownProjection`\n- category/count/staleness/warning enums, shared with the API through\n  `fabro-api` replacements if the serde shape matches.\n\nExtend `StageProjection` with:\n\n```rust\n#[serde(default, skip_serializing_if = \"Option::is_none\")]\npub context_window: Option<StageContextWindowProjection>,\n```\n\nDo not store raw content. The stored snapshot may contain only:\n\n- provider and model\n- context window size\n- input token total\n- category token counts\n- count method\n- generated timestamp\n- source event sequence\n- warning codes and messages\n\n## Events\n\nAdd a typed event in `lib/crates/fabro-types/src/run_event/agent.rs` and\n`lib/crates/fabro-types/src/run_event/mod.rs`:\n\n```text\nagent.context_window.snapshot\n```\n\nEvent payload should carry the same content-free counts as the projection plus\nthe stage id. The reducer in `lib/crates/fabro-store/src/run_state.rs` should\nreplace the selected stage's `context_window` with the latest snapshot.\n\nThis event is the durable fallback for inactive stages. It should be emitted\nwhen the agent assembles or refreshes the LLM request context, before the\nprovider request is sent. If a provider response later supplies better input\nusage for the same request, emit another snapshot using\n`response_usage_scaled_breakdown`.\n\n## Live Counting Design\n\nThe agent should produce snapshots using this decision order whenever it builds\nan LLM request:\n\n1. Build a content-free local category breakdown from the same inputs used to\n   assemble the `fabro_llm::Request`.\n2. Emit an immediate `agent.context_window.snapshot` with `local_estimate` so a\n   sidebar has data even if provider counting is slow or unavailable.\n3. Attempt provider counting with\n   `Client::count_input_tokens(..., InputTokenCountPreference::PreferProvider)`\n   using the exact in-memory request. This work must not persist or log the raw\n   request.\n4. If provider count succeeds, scale the local category estimates to the\n   provider total and emit a replacement snapshot with\n   `provider_api_scaled_breakdown`.\n5. If provider count falls back or fails, keep the local snapshot and include a\n   warning code on the next emitted snapshot. Count failures must not block the\n   agent's normal LLM request.\n\nThe API endpoint should follow this decision order:\n\n1. Validate the run and stage exist.\n2. If the stage is not an agent stage, return `available: false` with\n   `unavailable_reason: not_agent_stage`.\n3. Return the latest projected `StageContextWindowProjection`.\n4. If no snapshot has ever been observed, return `available: false` with\n   `unavailable_reason: not_observed`.\n\nDo not make the HTTP server own raw LLM requests. The existing server state\ntracks live run control and durable projections, while the exact request exists\ninside the active agent session. Provider counting should therefore happen in\nthe agent/worker process at request-assembly time, and the server endpoint\nshould expose the latest durable snapshot.\n\nIf a future implementation needs user-triggered refreshes, add a separate\nworker request/response control path. Do not tunnel raw request content through\nrun events or store it in `ManagedRun`.\n\nThe live request snapshot needs to be short-lived and content-safe:\n\n- Hold raw `Request` content only inside active agent sessions.\n- Never write that raw request to run events, projection state, logs, or API\n  responses.\n- Cache provider-count results by run id, stage id, provider, model, and request\n  fingerprint or source event seq so each LLM request is counted at most once.\n- Clear the live request handle when the session/stage deactivates.\n\n## Resolved Handoff Decisions\n\n### Decision 1: Category-Aware Estimation Ownership\n\nOptions:\n\n- Agent-only estimator: build all category counts in `fabro-agent`.\n- LLM-only estimator: move the full breakdown model into `fabro-llm`.\n- Hybrid estimator: keep category ownership in `fabro-agent`, but expose small\n  reusable token-estimation helpers from `fabro-llm`.\n\nRecommendation: use the hybrid estimator.\n\nJustification:\n\n- `fabro-agent` has the category knowledge. It sees memory documents, skills,\n  MCP registration, tool registry policy, and the final session history before\n  `Session::build_request` flattens everything into a generic LLM request.\n- `fabro-llm` has the token math and provider-neutral request structures. It\n  already owns local count behavior in `token_count.rs`, so duplicating that\n  estimator in `fabro-agent` would drift.\n- `fabro-llm` should not learn Fabro-specific categories like `skills` or\n  `memory`; that would couple a provider abstraction crate to agent UI\n  semantics.\n\nImplementation guidance:\n\n- Add a `fabro-agent/src/context_window.rs` builder that owns the category\n  taxonomy and content-free snapshot assembly.\n- Expose narrow helpers from `fabro-llm::token_count`, such as tool-definition\n  and message/content-part estimators, instead of making the private estimator\n  logic public wholesale.\n- Keep the existing `Client::count_input_tokens` provider call as the\n  authoritative total when available.\n\n### Decision 2: Provider Count Location\n\nOptions:\n\n- Server-side count: store or reconstruct the exact `fabro_llm::Request` in the\n  HTTP server and call the provider from the endpoint.\n- Synchronous worker query: add a request/response control channel so the HTTP\n  server can ask the active worker for a fresh count on demand.\n- Agent-side count on request assembly: the active session counts the exact\n  request it already has and emits content-free snapshots; the endpoint returns\n  the latest projection.\n\nRecommendation: use agent-side count on request assembly for the first\nimplementation.\n\nJustification:\n\n- It satisfies the requirement that provider counting is attempted by default,\n  because every active agent request can be counted once as it is assembled.\n- It avoids moving raw prompts, memory, tool results, or message history into\n  server-managed state.\n- The existing subprocess control path is one-way JSONL for actions like steer,\n  interrupt, and pair. Adding a synchronous query path just for this read model\n  would be more complex than emitting the durable projection the UI already\n  needs.\n- It works for both subprocess and in-process runs because the agent session is\n  the common place where the exact request exists.\n\nImplementation guidance:\n\n- Emit a local snapshot immediately, then emit a provider-scaled replacement if\n  provider counting succeeds.\n- Do not delay the LLM stream on provider counting unless implementation finds\n  that provider count latency is consistently negligible. A spawned count task\n  with the cloned request is acceptable as long as it is cancelled/ignored when\n  the session closes.\n- Treat provider count errors as snapshot warnings, not stage failures.\n\n### Decision 3: Memory and Skills Attribution\n\nOptions:\n\n- Keep the current flattened system prompt and count all prompt additions as\n  `system_prompt`.\n- Refactor prompt assembly to retain component boundaries, then estimate\n  memory and skills before the final prompt string is concatenated.\n- Add origin metadata to every history message and tool result so activated\n  skill instructions can be attributed even after entering the conversation.\n\nRecommendation: refactor prompt assembly for this first slice; defer full\nhistory-origin metadata.\n\nJustification:\n\n- `assemble_system_prompt` already receives `memory` and `skills` separately,\n  then concatenates them with the core prompt. Returning component metadata from\n  that boundary is a small, local change.\n- This gives useful and accurate first-slice attribution for loaded memory\n  files and the available-skills prompt without changing the persisted\n  conversation format.\n- Activated skill instructions are harder: slash expansion becomes a user turn,\n  and `use_skill` returns a tool result. The current `Message` enum does not\n  preserve source metadata. Adding it is possible, but it is a broader history\n  serialization migration and should not block the endpoint.\n\nImplementation guidance:\n\n- Count the base/core prompt as `system_prompt`.\n- Count memory document text appended by prompt assembly as `memory`.\n- Count the available-skills section and the `use_skill` tool definition as\n  `skills`.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` in this first implementation, with a warning such as\n  `activated_skill_context_counted_as_conversation` when such activations are\n  present.\n\n### Decision 4: Tool vs MCP Tool Attribution\n\nOptions:\n\n- Split MCP tools by name prefix, such as `mcp__`.\n- Add source metadata to `RegisteredTool` / `ToolRegistry`.\n- Recompute MCP membership from `McpConnectionManager` at request time.\n\nRecommendation: add source metadata to `RegisteredTool` / `ToolRegistry`.\n\nJustification:\n\n- The current registry stores only `ToolDefinition` plus executor, so origin is\n  lost after registration.\n- Prefix-based classification matches today's naming convention but is brittle\n  and will misclassify any future native tool that shares the prefix or any MCP\n  naming change.\n- `McpConnectionManager` has source knowledge during registration, but the\n  request builder only sees the final registry and policy-filtered tool\n  definitions.\n\nImplementation guidance:\n\n- Add a small `ToolSource` enum, for example `Native`, `Mcp { server_name }`,\n  and `Skill`.\n- Set `ToolSource::Mcp` in `mcp_integration::make_mcp_tools`.\n- Set `ToolSource::Skill` for `make_use_skill_tool`.\n- Keep existing public `definitions()` behavior unchanged; add a parallel\n  method that returns definitions with source metadata for context-window\n  accounting.\n\n### Decision 5: Unavailable and Error Semantics\n\nOptions:\n\n- Return 404/409 for non-agent stages or stages with no snapshot.\n- Return 200 with `available: false` for known stages where context-window data\n  is not applicable or not observed.\n- Return partial data with warnings for provider-count failures.\n\nRecommendation: return 200 with `available: false` for known-but-unavailable\ndata, and reserve HTTP errors for missing run/stage or malformed requests.\n\nJustification:\n\n- The sidebar needs to render stable empty states without treating normal\n  projection gaps as transport errors.\n- Provider-count support varies by provider and credentials; those are data\n  quality issues, not endpoint availability issues.\n- This matches the broader best-effort contract and avoids UI retry loops when\n  a completed older run simply has no context-window snapshot.\n\nImplementation guidance:\n\n- Use 404 only for missing run or missing stage.\n- Use `available: false` + `unavailable_reason` for `not_agent_stage`,\n  `not_observed`, or `provider_unconfigured`.\n- Use `warnings` for local estimate, provider fallback, ambiguous categories,\n  and activated skill content counted as conversation.\n\nThe previous open questions are resolved by these decisions.\n\n## Implementation Units\n\n### Unit 1: OpenAPI and Generated Clients\n\nFiles:\n\n- `docs/public/api-reference/fabro-api.yaml`\n- `lib/crates/fabro-api/build.rs`\n- `lib/crates/fabro-api/tests/`\n- `lib/packages/fabro-api-client/`\n\nTasks:\n\n- Add the path and schemas under Run Internals.\n- Prefer reusing hand-written Rust projection types through\n  `with_replacement(...)` when serde shape and semantics are identical.\n- Add a `fabro-api` test proving type identity and JSON parity for any new\n  replacement.\n- Regenerate Rust and TypeScript clients.\n\nTests:\n\n- `cargo build -p fabro-api`\n- `cargo nextest run -p fabro-api`\n- `cd lib/packages/fabro-api-client && bun run generate`\n\n### Unit 2: Content-Free Snapshot Builder\n\nFiles:\n\n- `lib/crates/fabro-llm/src/token_count.rs`\n- `lib/crates/fabro-agent/src/context_window.rs` (new)\n- `lib/crates/fabro-agent/src/session.rs`\n- `lib/crates/fabro-agent/src/profiles/mod.rs`\n- `lib/crates/fabro-agent/src/tool_registry.rs`\n- `lib/crates/fabro-agent/src/mcp_integration.rs`\n- `lib/crates/fabro-agent/src/skills.rs`\n- `lib/crates/fabro-agent/src/compaction.rs`\n- `lib/crates/fabro-agent/src/lib.rs`\n\nTasks:\n\n- Build category estimates at the same boundary where `Session::build_request`\n  assembles the `fabro_llm::Request`.\n- Expose narrow reusable token-estimation helpers from `fabro-llm` rather than\n  duplicating the estimator in `fabro-agent`.\n- Refactor prompt assembly enough to retain component boundaries for core\n  system prompt, memory, available skills, and user instructions before the\n  final prompt string is concatenated.\n- Add source metadata to registered tools so the builder can split native tools,\n  MCP tools, and skill-related tools after policy filtering.\n- Classify the system prompt separately from conversation history.\n- Count slash-expanded skill templates and `use_skill` tool results as\n  `conversation` for the first implementation, with a warning when activated\n  skill context is present.\n- Emit a local snapshot immediately and a provider-scaled replacement snapshot\n  when provider counting succeeds.\n- Keep local estimates deterministic and content-free.\n\nTests:\n\n- system prompt, tools, MCP tools, skills, memory, conversation, and other\n  categories are counted into the expected buckets.\n- category totals equal the local total before provider scaling.\n- provider-scaled totals add up to the provider total.\n- snapshots do not serialize prompt text, memory contents, tool arguments, or\n  message text.\n- opaque/ambiguous inputs produce warnings rather than silent misclassification.\n- provider count failure does not fail the agent turn.\n- each request fingerprint is counted by the provider at most once.\n\n### Unit 3: Event and Projection\n\nFiles:\n\n- `lib/crates/fabro-types/src/run_event/agent.rs`\n- `lib/crates/fabro-types/src/run_event/mod.rs`\n- `lib/crates/fabro-types/src/run_projection.rs`\n- `lib/crates/fabro-store/src/run_state.rs`\n\nTasks:\n\n- Add `agent.context_window.snapshot`.\n- Emit snapshots from the agent session path when requests are assembled and\n  when later response usage improves the count.\n- Project the latest snapshot onto `StageProjection.context_window`.\n- Preserve backwards-compatible deserialization for run projections that do not\n  have the new field.\n\nTests:\n\n- event `type_name()` returns `agent.context_window.snapshot`.\n- reducer updates only the matching stage.\n- later snapshots replace earlier snapshots for the same stage.\n- old projection JSON without `context_window` still deserializes.\n\n### Unit 4: Server Endpoint\n\nFiles:\n\n- `lib/crates/fabro-server/src/server/handler/mod.rs`\n- `lib/crates/fabro-server/src/server/handler/runs.rs` or a new\n  `context_window.rs` handler module\n- `lib/crates/fabro-server/src/server/tests.rs`\n\nTasks:\n\n- Add `GET /runs/{id}/stages/{stageId}/context-window`.\n- Use the same run-scoped authorization pattern as adjacent run internals.\n- Resolve run/stage from the cached projection first for fast 404s and stored\n  fallback.\n- Return the latest projected context-window snapshot for the stage.\n- Return `available: false` for known stages where context-window data is not\n  applicable or has not been observed.\n- Return 404 only for missing run/stage. Provider count failures are represented\n  as snapshot warnings because provider counting happens in the agent.\n\nTests:\n\n- missing run returns 404.\n- missing stage returns 404.\n- non-agent stage returns 200 with `available: false`.\n- projected provider-count success returns `staleness: live` and\n  `count_method: provider_api_scaled_breakdown`.\n- inactive stage returns the latest stored projection snapshot.\n- no observed snapshot returns `available: false` with `not_observed`.\n- projected warning payloads are returned without changing HTTP status.\n\n### Unit 5: Web Query Support\n\nFiles:\n\n- `apps/fabro-web/app/lib/query-keys.ts`\n- `apps/fabro-web/app/lib/queries.ts`\n- `apps/fabro-web/app/lib/run-events.ts`\n- `apps/fabro-web/app/lib/query-keys.test.ts`\n- `apps/fabro-web/app/lib/run-events.test.tsx`\n\nTasks:\n\n- Add `queryKeys.runs.stageContextWindow(id, stageId)`.\n- Add `useRunStageContextWindow(runId, stageId)` using the generated\n  TypeScript client.\n- Invalidate the context-window key for:\n  - `agent.context_window.snapshot`\n  - stage lifecycle events for the same stage\n  - agent activity events that can change the request context\n- Keep the full sidebar UI as follow-up work, but make the hook ready for the\n  agent-node page.\n\nTests:\n\n- query key encodes run id and stage id stably.\n- snapshot event invalidates the context-window key, run events, and stage\n  events.\n- stage lifecycle events invalidate the context-window key for the selected\n  stage.\n- activity events without a stage id do not invalidate unrelated stage context\n  windows.\n\n## Security and Privacy\n\n- Do not persist raw request content to make inactive-stage provider counting\n  possible.\n- Do not log prompt, memory, tool args, or message contents while computing\n  counts.\n- Warning messages should identify count quality, not repeat provider error\n  bodies if those bodies may contain request excerpts.\n- The endpoint should expose counts and category labels only.\n\n## Validation\n\nExpected validation after implementation:\n\n```bash\ncargo build -p fabro-api\ncargo nextest run -p fabro-api -p fabro-agent -p fabro-store -p fabro-server\ncd apps/fabro-web && bun test\ncd apps/fabro-web && bun run typecheck\ncargo +nightly-2026-04-14 fmt --check --all\ngit diff --check\n```\n\n## Remaining Follow-Ups\n\n- Full sidebar visualization.\n- Optional history-origin metadata if we later want activated skill templates\n  to move from `conversation` into `skills`.\n- Optional user-triggered live refresh path if future product needs require\n  provider counting on demand rather than at request assembly time.\n",
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              "last_response": "Implemented the context-window breakdown endpoint end-to-end.\n\n### What changed\n\n- Added OpenAPI contract for:\n  - `GET /api/v1/runs/{id}/stages/{stageId}/context-window`\n  - `StageContextWindow*` sch",
              "response.implement": "Implemented the context-window breakdown endpoint end-to-end.\n\n### What changed\n\n- Added OpenAPI contract for:\n  - `GET /api/v1/runs/{id}/stages/{stageId}/context-window`\n  - `StageContextWindow*` schemas/enums/warnings/breakdown DTOs.\n- Added content-free projection types in `fabro-types` and wired them through `fabro-api` replacements/tests.\n- Added typed event:\n  - `agent.context_window.snapshot`\n- Added reducer support in `fabro-store` so latest snapshots replace prior stage snapshots.\n- Added agent-side context-window accounting:\n  - local category breakdown at request assembly time\n  - async provider token count via `Client::count_input_tokens(... PreferProvider)`\n  - provider/API scaled replacement snapshots\n  - response-usage scaled replacement snapshots\n  - content-safe request fingerprinting and close-token cancellation for count tasks\n- Added tool source metadata (`Native`, `Mcp`, `Skill`) so tool definitions split into native tools, MCP tools, and skills.\n- Exposed narrow token-estimation helpers from `fabro-llm`.\n- Added server endpoint behavior:\n  - 404 for missing run/stage\n  - `available: false` for non-agent stages and unobserved agent stages\n  - stored/latest projected snapshot response with warnings preserved\n- Added web query support:\n  - `queryKeys.runs.stageContextWindow(id, stageId)`\n  - `useRunStageContextWindow(runId, stageId)`\n  - SSE invalidation for context-window snapshots, stage lifecycle, steering/activity, and todo activity.\n- Updated TypeScript generated client files manually for the new endpoint/models because the generator could not run in this environment.\n\n### Validation run\n\nPassed:\n\n- `cargo build -q -p fabro-api`\n- `cargo check -q --workspace`\n- `cargo nextest run -p fabro-types -p fabro-store -p fabro-api --no-fail-fast`\n- `cargo nextest run -p fabro-agent --no-fail-fast`\n- `cargo nextest run -p fabro-workflow --no-fail-fast`\n- `cargo nextest run -p fabro-server get_run_stage_context_window --no-fail-fast`\n- `cargo +nightly-2026-04-14 fmt --check --all`\n- `cargo +nightly-2026-04-14 clippy --workspace --all-targets -- -D warnings`\n- `git diff --check`\n- `cd apps/fabro-web && bun test app/lib/query-keys.test.ts app/lib/run-events.test.tsx`\n- `cd apps/fabro-web && bun run typecheck`\n- `cd apps/fabro-web && bun test app/lib/run-actions.test.ts`\n\nNotes:\n\n- `cd lib/packages/fabro-api-client && bun run generate` failed because the environment lacks `java`; `bunx` latest also hit a Node 18 ESM issue. I manually updated the generated TypeScript client/model files to match the OpenAPI change.\n- Full `cd apps/fabro-web && bun test` still reports failures in `run-actions.test.ts` only when run as part of the entire suite; that file passes in isolation. This appears to be pre-existing/global test-state interference, not caused by the context-window changes.\n- Full `cargo nextest run -p fabro-server` previously surfaced unrelated graph-render subprocess failures; the new context-window endpoint tests pass directly."
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              "response.simplify_opus": "All changes complete. Here's a summary of what was fixed.\n\n## Summary\n\nThree review agents identified findings across reuse, quality, and efficiency. I applied the highest-impact, clearly-correct fixes and skipped the ones that turned out to be wrong on closer inspection (e.g. cache-token math; the reviewer was mistaken — input_tokens excludes cache tokens for all three providers, so the original addition is correct).\n\n### Fixes applied\n\n1. **Web invalidation scope** (`apps/fabro-web/app/lib/run-events.ts` + tests): the context-window endpoint's projection only changes on `agent.context_window.snapshot` events, so `stageContextWindow` invalidation was removed from `STAGE_EVENTS`, `STEERING_EVENTS`, `STAGE_ACTIVITY_EVENTS`, and `TODO_EVENTS` branches. Saves an HTTP refetch on every agent activity event.\n\n2. **Reuse `StageState::is_terminal()`** (`lib/crates/fabro-server/src/server/handler/runs.rs`): replaced the local `is_live_stage` helper with `stage.state.is_terminal()` and dropped the unused `StageState` import.\n\n3. **Consolidate breakdown item type** (`lib/crates/fabro-types/src/run_projection.rs` + OpenAPI + generated TS): dropped the `StageContextWindowBreakdownItem` type alias by renaming `StageContextWindowBreakdownProjection` to `StageContextWindowBreakdownItem`. Per CLAUDE.md API DTO guidance.\n\n4. **Dropped redundant `label` and `source` fields** from breakdown items (OpenAPI, Rust projection, generated TS, agent estimator, all test fixtures). `label` was UI text in the API; `source` duplicated the outer `count_method`.\n\n5. **Simplified `scale_breakdown`** (`lib/crates/fabro-agent/src/context_window.rs`): replaced ~60-line largest-remainder algorithm with a simple proportional scale where the last bucket absorbs the rounding leftover. Acceptable for a best-effort estimate.\n\n6. **Consolidated `ToolRegistry` policy filters** (`lib/crates/fabro-agent/src/tool_registry.rs`): `definitions_for_policy` now delegates to `definitions_with_source_for_policy`, removing the duplicated filter logic.\n\n### Validation\n\n- `cargo +nightly-2026-04-14 clippy --workspace --all-targets -- -D warnings`: clean\n- `cargo +nightly-2026-04-14 fmt --check --all`: clean\n- `cargo nextest run -p fabro-agent -p fabro-api -p fabro-store -p fabro-server -p fabro-types`: 1674 passed, 3 failed (all pre-existing graphviz subprocess failures unrelated to this change, confirmed by running the same tests on `origin/main`).\n- `apps/fabro-web bun test app/lib/query-keys.test.ts app/lib/run-events.test.tsx`: 21/21 pass\n- `apps/fabro-web bun run typecheck`: clean\n\n### Findings deliberately skipped\n\n- **Drop spawned provider-API count**: per Decision 2 of the original plan, provider counting at request assembly is explicitly required. Removing it would deviate from the spec.\n- **Cache-token math (`input + cache_read + cache_write`)**: confirmed correct by reading each provider's adapter — OpenAI/Gemini explicitly `saturating_sub(cached_tokens)` from `input_tokens`, and Anthropic's `input_tokens` is documented as uncached. Sum is the right total context size.\n- **Per-message token cache (O(N²) concern)**: real but the estimator already short-circuits zero-token additions and only runs once per agent turn; deferring this since it would add cache invalidation complexity to a best-effort feature.\n- **Bigger DTO/projection unification (`StageContextWindow` flattens projection)**: legitimate but a larger refactor that risks the response shape; left in place since current shape is already covered by tests and the build.rs round-trip.\n- **Several lower-severity findings** (e.g. `BuiltRequest` wrapper, `close_token` field, `request_fingerprint` smell) are noise compared to the size of the diff; not worth the churn in a greenfield-priority pass that didn't elect to drop the provider count path itself.",
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        "reasoning_tokens": 0,
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      "model": {
        "provider": "anthropic",
        "model_id": "claude-opus-4-7"
      },
      "todos": {
        "kind": "anthropic_tasks",
        "list_id": "anthropic_tasks:e4119611-bf8c-4ee3-8776-bc630103c95f",
        "items": [
          {
            "id": "1",
            "status": "completed",
            "order": 0,
            "subject": "Scope web invalidation to context_window.snapshot only",
            "description": "Remove stageContextWindow from STAGE_EVENTS/STEERING_EVENTS/STAGE_ACTIVITY_EVENTS/TODO_EVENTS branches in run-events.ts. Only the snapshot event changes the projection."
          },
          {
            "id": "2",
            "status": "completed",
            "order": 1,
            "subject": "Use StageState::is_terminal instead of is_live_stage helper",
            "description": "Replace `is_live_stage(state)` with `!stage.state.is_terminal()` in server handler runs.rs and delete the helper."
          },
          {
            "id": "3",
            "status": "completed",
            "order": 2,
            "subject": "Drop redundant label/source fields from breakdown item and rename type to StageContextWindowBreakdownItem",
            "description": "Per CLAUDE.md API DTO guidance: drop the `StageContextWindowBreakdownItem` type alias by renaming `StageContextWindowBreakdownProjection` to `StageContextWindowBreakdownItem`. Drop `label` (UI text) and `source` (redundant with outer count_method). Update OpenAPI, Rust types, generated TS client, tests, agent estimator, and round-trip test."
          },
          {
            "id": "4",
            "status": "completed",
            "order": 3,
            "subject": "Simplify scale_breakdown to a simple proportional scale",
            "description": "Replace 60-line largest-remainder algorithm with simple proportional scale where the last bucket absorbs rounding. Best-effort estimate doesn't need perfect apportionment."
          },
          {
            "id": "5",
            "status": "completed",
            "order": 4,
            "subject": "Consolidate ToolRegistry definitions_for_policy with source variant",
            "description": "Have a single `definitions_with_source_for_policy` method (or rename) and let callers that want bare ToolDefinitions map. Removes near-duplicate filter logic."
          }
        ]
      },
      "subagents": [
        {
          "agent_id": "df655675",
          "depth": 1,
          "task": "You are performing a CODE REUSE REVIEW on a Rust + TypeScript codebase. The diff vs origin/main is saved to `.fabro_diff.txt` in the working directory.\n\nThe change adds a \"context window breakdown\" endpoint:\n- New file `lib/crates/fabro-agent/src/context_window.rs` (~484 lines) - category estimator\n- Modifications to `lib/crates/fabro-llm/src/token_count.rs` exposing reusable estimator helpers\n- New types in `lib/crates/fabro-types/src/run_projection.rs`\n- New event `agent.context_window.snapshot`\n- New server endpoint `GET /runs/{id}/stages/{stageId}/context-window`\n- Generated OpenAPI types and TypeScript client\n- New web query hook\n\nYOUR JOB:\n1. Read the diff in `.fabro_diff.txt`.\n2. For each substantive change, use Grep to search for existing utilities / helpers / patterns in the repo that could replace newly-written code.\n3. Flag duplication of existing functionality, ad-hoc inline logic that could use an existing utility, and parallel implementations.\n\nCommon search locations:\n- `lib/crates/fabro-util/src/`\n- `lib/crates/fabro-types/src/`\n- `lib/crates/fabro-llm/src/token_count.rs`\n- `lib/crates/fabro-store/src/`\n- `lib/crates/fabro-server/src/server/handler/`\n- `apps/fabro-web/app/lib/`\n\nSpecifically check:\n- Token estimation helpers in `fabro-llm/src/token_count.rs` - is anything in the new `context_window.rs` reinventing them?\n- Stage projection lookup helpers - is there an existing helper to look up a stage by id from a RunProjection?\n- Run/stage resolution patterns in server handlers - is there a shared helper for \"load run + find stage + 404\"?\n- Web query invalidation patterns in `run-events.ts` - any similar event-based invalidation already in place?\n- Enum (de)serialization helpers - does the codebase have a shared pattern for context-window-style enums?\n- Warning/snapshot construction patterns in fabro-agent.\n\nOUTPUT FORMAT: A concise bulleted list of findings. For each, give:\n- File and approximate location\n- The duplication or missed reuse\n- The existing utility/pattern to use instead (file path + name)\n- Severity (high/med/low)\n\nSkip false positives. Be specific. Do not write code - just identify issues.",
          "status": {
            "kind": "completed",
            "success": true,
            "turns_used": 94
          }
        },
        {
          "agent_id": "8800b3c7",
          "depth": 1,
          "task": "You are performing a CODE QUALITY REVIEW on a Rust + TypeScript codebase. The diff vs origin/main is saved to `.fabro_diff.txt` in the working directory.\n\nThe change adds a \"context window breakdown\" endpoint:\n- New file `lib/crates/fabro-agent/src/context_window.rs` (~484 lines) - category estimator\n- Modifications to `lib/crates/fabro-llm/src/token_count.rs` exposing reusable estimator helpers\n- New types in `lib/crates/fabro-types/src/run_projection.rs`\n- New event `agent.context_window.snapshot`\n- New server endpoint `GET /runs/{id}/stages/{stageId}/context-window`\n- Generated OpenAPI types and TypeScript client\n- New web query hook\n\nYOUR JOB: Read the diff and review for hacky patterns. Note this is greenfield - be aggressive in optimizing quality.\n\nLook for:\n1. Redundant state: state duplicating existing state, cached values that could be derived\n2. Parameter sprawl: new parameters added piecemeal vs. restructuring\n3. Copy-paste with slight variation: near-duplicate blocks that should share an abstraction\n4. Leaky abstractions: exposing internal details, breaking encapsulation, raw_request handling\n5. Stringly-typed code: raw strings where enums/constants already exist\n6. Verbose `as f64` math that could be a single helper (e.g., `usage_percent` calculation duplicated across DTO, projection, API layer)\n7. Three nearly-identical structs (`StageContextWindow` DTO, `StageContextWindowProjection`, generated API type) - are these actually distinct semantically, or did the implementer split them unnecessarily?\n8. Manual From/Into conversions that suggest the types should be unified\n9. Handcrafted reducer logic in `lib/crates/fabro-store/src/run_state.rs` for the new event that could reuse projection patterns\n10. The category estimator: is it doing manual splits that could use existing tokenizer abstractions?\n\nPay particular attention to:\n- `lib/crates/fabro-agent/src/context_window.rs` - is the estimator over-engineered for a \"best-effort\" count?\n- The split between `StageContextWindow` (API DTO in handler) and `StageContextWindowProjection` (in run_projection.rs) - per CLAUDE.md, API types should reuse projections via `with_replacement(...)` when serde shape matches\n- Conversion functions (`*_to_api`, `From<X> for Y`) - per CLAUDE.md these are a smell unless representing a real semantic boundary\n- The server handler in `runs.rs` - is the not_agent_stage detection logic duplicated/leaky?\n\nOUTPUT FORMAT: Concise bulleted list of findings with file/line refs and severity (high/med/low). Skip false positives.",
          "status": {
            "kind": "completed",
            "success": true,
            "turns_used": 33
          }
        },
        {
          "agent_id": "39688d1c",
          "depth": 1,
          "task": "You are performing an EFFICIENCY REVIEW on a Rust + TypeScript codebase. The diff vs origin/main is saved to `.fabro_diff.txt` in the working directory.\n\nThe change adds a \"context window breakdown\" endpoint that emits snapshots when the agent assembles each LLM request:\n- New file `lib/crates/fabro-agent/src/context_window.rs` - category estimator that runs at every request\n- Modifications to `lib/crates/fabro-agent/src/session.rs` calling the estimator on each request build\n- New event `agent.context_window.snapshot` (emitted twice per request: local then provider-scaled)\n- Reducer in `lib/crates/fabro-store/src/run_state.rs`\n- Server endpoint `GET /runs/{id}/stages/{stageId}/context-window`\n- Web query/SSE invalidation\n\nYOUR JOB: Read the diff and review for efficiency. Specifically check:\n\n1. Unnecessary work: \n   - Does the category estimator do repeated work that scales with conversation history on every turn?\n   - Does it re-scan tool definitions on every request when they don't change?\n   - Are tokens being counted twice (once locally, once via provider) for every turn even when nothing changed?\n   - The provider counting cache by fingerprint - is the fingerprint computed efficiently?\n\n2. Missed concurrency:\n   - Does the local snapshot emission block the LLM request?\n   - Does the provider count run concurrent with the LLM request, or block it?\n\n3. Hot-path bloat:\n   - The estimator runs on every `Session::build_request` - any heavy work added?\n   - Per-render/per-SSE-event work in the web client invalidation?\n\n4. Memory:\n   - Unbounded caches? (e.g., the per-request fingerprint cache)\n   - Cloned `Request` objects sent to count tasks - are they dropped promptly?\n   - The reducer appending unbounded warnings list?\n\n5. Over-broad invalidation:\n   - In `apps/fabro-web/app/lib/run-events.ts`, does context-window invalidation fire on too many events?\n   - Does any stage event invalidate all stages instead of just the one that changed?\n\n6. JSON serialization:\n   - Are snapshots over-large (e.g., per-tool counts) when only category totals are needed?\n\nLook specifically at:\n- `lib/crates/fabro-agent/src/session.rs` for where the estimator and emission are called\n- `lib/crates/fabro-agent/src/context_window.rs` for the estimator loops\n- `lib/crates/fabro-store/src/run_state.rs` for reducer cost\n- `apps/fabro-web/app/lib/run-events.ts` for invalidation breadth\n\nOUTPUT FORMAT: Concise bulleted list of findings with file/line refs and severity (high/med/low). Skip false positives.",
          "status": {
            "kind": "completed",
            "success": true,
            "turns_used": 35
          }
        }
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      "response": null,
      "completion": {
        "outcome": "succeeded",
        "notes": "Stage completed: implement",
        "failure_reason": null,
        "timestamp": "2026-05-23T21:02:59.711701Z"
      },
      "provider_used": {
        "mode": "agent",
        "provider": "openai",
        "model": "gpt-5.5",
        "reasoning_effort": "xhigh"
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      "diff": null,
      "script_invocation": null,
      "script_timing": null,
      "parallel_results": null,
      "output": null,
      "started_at": "2026-05-23T20:17:45.290369Z",
      "handler": "agent",
      "timing": {
        "wall_time_ms": 2714402,
        "inference_time_ms": 0,
        "tool_time_ms": 0,
        "active_time_ms": 0
      },
      "usage": {
        "input_tokens": 919482,
        "output_tokens": 49948,
        "total_tokens": 84797402,
        "reasoning_tokens": 15620,
        "cache_read_tokens": 83812352,
        "cache_write_tokens": 0,
        "total_usd_micros": 48470626
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      "model": {
        "provider": "openai",
        "model_id": "gpt-5.5"
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      "todos": {
        "kind": "openai_plan",
        "list_id": "openai_plan:4559f662-dc05-499f-9c10-2e0f2bdd50ed",
        "items": [
          {
            "id": "f3b91102edcd45b8",
            "status": "completed",
            "order": 0,
            "subject": "Read repository guidance and strategy docs relevant to events, errors, and tests"
          },
          {
            "id": "fb45b34d3dd547de",
            "status": "completed",
            "order": 1,
            "subject": "Inspect existing projection, event, reducer, agent request assembly, API generation, server route, and web query patterns"
          },
          {
            "id": "aae477f502d2dc62",
            "status": "completed",
            "order": 2,
            "subject": "Add red tests for projection/event/reducer/API/server/web support where practical"
          },
          {
            "id": "09ec0c689e7d7198",
            "status": "completed",
            "order": 3,
            "subject": "Implement projection DTOs, event payloads, reducer updates, and API replacements/schemas"
          },
          {
            "id": "6373440ebe2aac88",
            "status": "completed",
            "order": 4,
            "subject": "Implement agent-side context-window local snapshot builder and provider-scaled snapshot emission"
          },
          {
            "id": "7b2f85aeb30f22dd",
            "status": "completed",
            "order": 5,
            "subject": "Implement server context-window endpoint and generated clients"
          },
          {
            "id": "af4f29ded2bfc938",
            "status": "completed",
            "order": 6,
            "subject": "Implement web query key/hook/SSE invalidation support"
          },
          {
            "id": "78913d21ccda691d",
            "status": "completed",
            "order": 7,
            "subject": "Run targeted tests/format/lint checks, fix failures, and summarize changes"
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}