diff --git a/run.json b/run.json index 8a5ed0f9a..5c6241756 100644 --- a/run.json +++ b/run.json @@ -494,7 +494,7 @@ "kind": "running" }, "status_updated_at": "2026-05-23T20:13:21.055223Z", - "last_event_at": "2026-05-23T20:13:26.986402Z", + "last_event_at": "2026-05-23T20:15:30.436886Z", "pending_control": null, "checkpoints": [ { @@ -592,9 +592,9 @@ } }, { - "seq": 0, + "seq": 40, "checkpoint": { - "timestamp": "2026-05-23T20:15:26.776601Z", + "timestamp": "2026-05-23T20:15:30.433170Z", "current_node": "preflight_compile", "completed_nodes": [ "start", @@ -602,18 +602,90 @@ "preflight_compile" ], "node_retries": {}, + "context_values": { + "graph.model_stylesheet": "\n * { model: claude-opus-4-7; }\n ", + "graph.rankdir": "LR", + "internal.retry_count.start": 0, + "internal.work_dir": "/home/daytona/workspace/fabro", + "thread.start.current_node": "toolchain", + "internal.fidelity": "compact", + "internal.retry_count.preflight_compile": 0, + "internal.retry_count.toolchain": 0, + "internal.node_visit_count": 1, + "thread.toolchain.current_node": "preflight_compile", + "failure_class": "", + "outcome": "succeeded", + "current_node": "preflight_compile", + "failure_signature": "", + "command.output": "blob://sha256/12ae32cb1ec02d01eda3581b127c1fee3b0dc53572ed6baf239721a03d82e126", + "internal.thread_id": "toolchain", + "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,\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", + "internal.run_id": "01KSB7GKM0A8P61YYCV7WNYJG9" + }, + "node_outcomes": { + "start": { + "status": "succeeded", + "usage": null + }, + "preflight_compile": { + "status": "succeeded", + "context_updates": { + "command.output": "blob://sha256/12ae32cb1ec02d01eda3581b127c1fee3b0dc53572ed6baf239721a03d82e126" + }, + "notes": "Script completed: cargo check -q --workspace 2>&1", + "usage": null + }, + "toolchain": { + "status": "succeeded", + "context_updates": { + "command.output": "blob://sha256/fc14b2ba2d770e5cd3169df7a29525c962adfc4cfa3097b9098c63ebd61a748c" + }, + "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", + "usage": null + } + }, + "next_node_id": "preflight_lint", + "git_commit_sha": "77d40b5d20ec5e896f559c151a3eaeb27a86206c", + "node_visits": { + "toolchain": 1, + "preflight_compile": 1, + "start": 1 + } + }, + "diff": { + "summary": { + "files_changed": 0, + "additions": 0, + "deletions": 0 + } + } + }, + { + "seq": 0, + "checkpoint": { + "timestamp": "2026-05-23T20:17:41.533767Z", + "current_node": "preflight_lint", + "completed_nodes": [ + "start", + "toolchain", + "preflight_compile", + "preflight_lint" + ], + "node_retries": {}, "context_values": { "internal.retry_count.preflight_compile": 0, "internal.node_visit_count": 1, "internal.fidelity": "compact", - "internal.thread_id": "toolchain", + "internal.thread_id": "preflight_compile", "graph.rankdir": "LR", + "thread.preflight_compile.current_node": "preflight_lint", "internal.retry_count.start": 0, + "internal.retry_count.preflight_lint": 0, "internal.run_id": "01KSB7GKM0A8P61YYCV7WNYJG9", "internal.retry_count.toolchain": 0, "outcome": "succeeded", "graph.model_stylesheet": "\n * { model: claude-opus-4-7; }\n ", - "current_node": "preflight_compile", + "current_node": "preflight_lint", "thread.start.current_node": "toolchain", "command.output": "blob://sha256/12ae32cb1ec02d01eda3581b127c1fee3b0dc53572ed6baf239721a03d82e126", "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,\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", @@ -623,6 +695,14 @@ "thread.toolchain.current_node": "preflight_compile" }, "node_outcomes": { + "preflight_lint": { + "status": "succeeded", + "context_updates": { + "command.output": "blob://sha256/12ae32cb1ec02d01eda3581b127c1fee3b0dc53572ed6baf239721a03d82e126" + }, + "notes": "Script completed: cargo +nightly-2026-04-14 clippy -q --workspace --all-targets -- -D warnings 2>&1", + "usage": null + }, "preflight_compile": { "status": "succeeded", "context_updates": { @@ -644,11 +724,12 @@ "usage": null } }, - "next_node_id": "preflight_lint", + "next_node_id": "implement", "node_visits": { - "toolchain": 1, "preflight_compile": 1, - "start": 1 + "preflight_lint": 1, + "start": 1, + "toolchain": 1 } }, "diff": {} @@ -760,7 +841,12 @@ "first_event_seq": 33, "prompt": null, "response": null, - "completion": null, + "completion": { + "outcome": "succeeded", + "notes": "Script completed: cargo check -q --workspace 2>&1", + "failure_reason": null, + "timestamp": "2026-05-23T20:15:26.775075Z" + }, "provider_used": null, "diff": null, "script_invocation": { @@ -768,10 +854,53 @@ "command": "exec 2>&1\ncargo check -q --workspace 2>&1", "language": "shell" }, + "script_timing": { + "output": "blob://sha256/12ae32cb1ec02d01eda3581b127c1fee3b0dc53572ed6baf239721a03d82e126", + "exit_code": 0, + "duration_ms": 119778, + "termination": "exited", + "output_bytes": 0, + "live_streaming": false + }, + "parallel_results": null, + "output": null, + "output_bytes": 0, + "live_streaming": false, + "termination": "exited", + "started_at": "2026-05-23T20:13:26.986103Z", + "handler": "command", + "timing": { + "wall_time_ms": 119787, + "inference_time_ms": 0, + "tool_time_ms": 0, + "active_time_ms": 0 + }, + "usage": { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "reasoning_tokens": 0, + "cache_read_tokens": 0, + "cache_write_tokens": 0 + }, + "state": "succeeded" + }, + "preflight_lint@1": { + "first_event_seq": 43, + "prompt": null, + "response": null, + "completion": null, + "provider_used": null, + "diff": null, + "script_invocation": { + "script": "cargo +nightly-2026-04-14 clippy -q --workspace --all-targets -- -D warnings 2>&1", + "command": "exec 2>&1\ncargo +nightly-2026-04-14 clippy -q --workspace --all-targets -- -D warnings 2>&1", + "language": "shell" + }, "script_timing": null, "parallel_results": null, "output": null, - "started_at": "2026-05-23T20:13:26.986103Z", + "started_at": "2026-05-23T20:15:30.436296Z", "handler": "command", "usage": { "input_tokens": 0, diff --git a/stages/003-preflight_compile@1/output.log b/stages/003-preflight_compile@1/output.log new file mode 100644 index 000000000..d87ba9545 --- /dev/null +++ b/stages/003-preflight_compile@1/output.log @@ -0,0 +1 @@ +blob://sha256/12ae32cb1ec02d01eda3581b127c1fee3b0dc53572ed6baf239721a03d82e126 \ No newline at end of file diff --git a/stages/003-preflight_compile@1/script_timing.json b/stages/003-preflight_compile@1/script_timing.json new file mode 100644 index 000000000..04041c9b6 --- /dev/null +++ b/stages/003-preflight_compile@1/script_timing.json @@ -0,0 +1,8 @@ +{ + "output": "blob://sha256/12ae32cb1ec02d01eda3581b127c1fee3b0dc53572ed6baf239721a03d82e126", + "exit_code": 0, + "duration_ms": 119778, + "termination": "exited", + "output_bytes": 0, + "live_streaming": false +} \ No newline at end of file diff --git a/stages/003-preflight_compile@1/status.json b/stages/003-preflight_compile@1/status.json new file mode 100644 index 000000000..423a2e11c --- /dev/null +++ b/stages/003-preflight_compile@1/status.json @@ -0,0 +1,6 @@ +{ + "outcome": "succeeded", + "notes": "Script completed: cargo check -q --workspace 2>&1", + "failure_reason": null, + "timestamp": "2026-05-23T20:15:26.775075Z" +} \ No newline at end of file diff --git a/stages/004-preflight_lint@1/script_invocation.json b/stages/004-preflight_lint@1/script_invocation.json new file mode 100644 index 000000000..0cb6a9faa --- /dev/null +++ b/stages/004-preflight_lint@1/script_invocation.json @@ -0,0 +1,5 @@ +{ + "script": "cargo +nightly-2026-04-14 clippy -q --workspace --all-targets -- -D warnings 2>&1", + "command": "exec 2>&1\ncargo +nightly-2026-04-14 clippy -q --workspace --all-targets -- -D warnings 2>&1", + "language": "shell" +} \ No newline at end of file