GitNexus/eval/workflow_bench

Skill benchmark — evolve review quality, measure workflow cost

Measures whether the gitnexus-plan → gitnexus-work engineering workflow actually saves tokens versus a baseline agent on the same tasks, using real headless Claude Code sessions. Nothing is estimated: every number comes from the CLI's own final event in its parent-captured --output-format stream-json report.

What it compares

Arm	Sessions	Notes
workflow	gitnexus-plan on the task, then gitnexus-work on the produced plan	The skills must be installed (gitnexus setup, or repo-local .claude/skills/)
candidate_workflow	same sessions as workflow, with a candidate skill overlay	Paired with workflow on the same task/ref/model
workflow_direct	one gitnexus-work direct-mode session	The middle option — execution discipline without a planning pass
candidate_workflow_direct	same session as workflow_direct, with a candidate skill overlay	Paired with workflow_direct on the same task/ref/model
ce_workflow	ce-plan on the task, then ce-work on the produced plan	External comparator: the explicitly supplied, pinned compound-engineering plugin's plan→work family
ce_workflow_direct	one ce-work direct-mode session	External comparator paired with workflow_direct
review	one gitnexus-review session over an immutable historical PR snapshot	Emits strict review-output.json; hidden human labels score quality after the session
candidate_review	the same review with a gitnexus-review candidate overlay	Paired with review on the same case/ref/model/runtime
ce_review	one pinned ce-code-review session over the same changes	External comparator paired with both review arms
baseline	one session with the identical task text	--disallowedTools Skill so it cannot borrow the workflow; same repo, same MCP tools
baseline_nomcp	like baseline, graph tools also disallowed	Separates the workflow-discipline question from the GitNexus-tools question (off by default)

Every arm runs in a fresh detached git worktree of the task's ref, once per --runs. The model-visible verify command is recorded as authored_tests_passed, but cannot certify its own solution: resolved also requires the task's harness-owned hidden behavioral oracle to pass. Token savings on a failed task are flagged, not celebrated, and diff churn (files/+insertions/−deletions vs the starting commit) is recorded as a cheap over-engineering proxy. Task class labels (trivial → investigation → cross-module) make the report readable as a routing table: the boundary where workflow starts beating workflow_direct and baseline is the boundary lfg's gate and work's direct-mode triage should encode.

Quick start

cd eval
export GITNEXUS_BENCH_ANTHROPIC_API_KEY="$ANTHROPIC_API_KEY"
uv run --locked --extra dev python -m workflow_bench.runner \
  --tasks workflow_bench/tasks.scenarios.yaml --runs 3 \
  --model claude-sonnet-4-20250514

Scenarios marked expensive: true are skipped unless --include-expensive is supplied. The report names both selected and skipped tasks so an omitted cell cannot be mistaken for evidence.

CE comparator arms never discover a user-level plugin. Supply an exact plugin release explicitly; both flags are mandatory whenever any ce_* arm is selected:

uv run --locked --extra dev python -m workflow_bench.runner \
  --tasks workflow_bench/tasks.scenarios.yaml --runs 3 \
  --model claude-sonnet-4-20250514 \
  --arms workflow ce_workflow \
  --ce-plugin-dir /opt/operator-input/compound-engineering-3.19.0 \
  --ce-plugin-version 3.19.0

The runner verifies the manifest version, copies only the plugin manifests, skills, scripts, and assets into a bounded no-symlink snapshot, and mounts that snapshot read-only only for CE arms. Every CE result records its exact plugin version and content-manifest digest.

Output: results/wfbench-<timestamp>/results.jsonl (every run, with session ids for transcript drill-down) and report.md (medians per task per arm, plus a savings row: input / cache / output tokens, cost, wall time).

Trust model — fail-closed Linux containment

Task files and candidate prose remain untrusted executable inputs. Every setup, verifier, incumbent, and candidate cell therefore runs in a preflighted Bubblewrap boundary with a private home/config/temp, a self-contained clone, a PID namespace, bounded process-tree ownership, and a deny-by-default environment. Task-declared dependency roots are mounted read-only, while graph assets are rebuilt by the harness as described below. Claude runs in bare, dontAsk mode with strict clone-local MCP configuration; Bash children do not inherit the model credential and their network sandbox denies all domains.

Prebuilt task .gitnexus assets are rejected. For each task commit, the harness creates the deterministic parentless snapshot first, removes every analyzer-visible path or stored source reference to the benchmark harness, neutralizes target-controlled GitNexus config/ignore files, and builds one fresh PDG index offline with --pdg --index-only --no-stats. It then proves that neither whole graph nodes nor relationships contain a harness marker and caches only the bound metadata/database assets for reuse by paired arms.

Each selected task also declares a bounded hidden oracle command and file set. The harness captures those regular, non-symlink files into an immutable in-memory snapshot before any arm runs and binds the command, paths, sizes, and raw bytes into the task digest. Before any task asset or model session, each disposable clone that contains the benchmark harness is rewritten to a clean, parentless snapshot without eval/workflow_bench; all original refs, reflogs, and unreachable Git objects are pruned so git show cannot recover the hidden bytes. Only after the model exits (and after the authored-test signal is collected) does the harness materialize the oracle beneath a private host root, mount it read-only at a random workspace sibling, and supply that mount through GITNEXUS_BENCH_ORACLE_ROOT. This layout preserves hidden tests' ../gitnexus imports as the credited candidate checkout. Authored and hidden verifiers run with the complete workspace read-only and networking unshared; hidden stdout/stderr is never persisted. The harness re-checks every oracle byte and erases the mountpoint before churn/patch capture. Shipped Vitest oracles use the staged, digest-bound vitest.config.mts; a candidate cannot replace repo test config or setup hooks to make the hidden test vacuously pass. The hidden command invokes the read-only dependency's Vitest binary directly, without an npx configuration/resolution layer.

Every evaluated repo-local skill root is over-mounted read-only for the full model session, and an immutable empty user-level skills directory prevents a writable $HOME skill from shadowing it. Skill-use evidence comes only from the bounded stream captured directly from Claude stdout by the parent. The runner parses every event through EOF, requires one final result, correlates an exact Skill request ID with one later successful result, structurally redacts the event objects, and stores the canonical redacted JSONL with a digest. Files written beneath the agent's $HOME are never trusted as evidence.

Bare mode is deliberately non-interactive: it does not consult a stored Claude login/keychain or ANTHROPIC_AUTH_TOKEN. Supply an Anthropic API key through GITNEXUS_BENCH_ANTHROPIC_API_KEY (preferred) or --anthropic-api-key; the harness maps it to ANTHROPIC_API_KEY only for the trusted Claude parent and scrubs it from agent-launched tools. GITNEXUS_BENCH_AUTH_TOKEN and --auth-token remain as aliases. OpenAI keys are not a drop-in replacement: pass --openai-api-key / GITNEXUS_BENCH_OPENAI_API_KEY with gpt-* / o* / openai/* model ids and the harness starts a loopback LiteLLM proxy. The OpenAI key stays on that host process; Claude still sees only a minted ANTHROPIC_API_KEY plus ANTHROPIC_BASE_URL.

The trusted Claude CLI still needs outbound access to the explicitly supplied model endpoint. This is not a network broker, so the CLI itself retains that egress; agent-launched tools do not. Missing Bubblewrap, unsupported hosts, invalid mounts, or namespace preflight failure stop before model invocation. Native benchmark execution is therefore Linux/WSL2-only. Evidence assembly and hand-authored overlay preparation can happen elsewhere, but --initial-overlay does not bypass containment.

For a local diagnostic inside a container that blocks user namespaces, an operator may explicitly choose the non-containment host backend:

UNSAFE_NO_BWRAP=1 RUNS=1 ./workflow_bench/run-evolution.sh

This mode runs review sessions directly in disposable host worktrees and is not a security boundary: it does not isolate the network or create a PID namespace, and a session that can chmod can undo the workspace lock. The harness still drops write bits on the clone except review-output.json so accidental npm install / analyze writes cannot invalidate review evidence. Sandbox cleanup restores owner write bits before deleting the private TMPDIR, because a session that copytrees the locked clone would otherwise leave non-empty 0555 directories that rmtree cannot remove. Historical review SHAs that gitignore .claude/skills/* are force-added when the harness seeds or overlays the evaluated gitnexus-review skill. Treat model and verifier processes as able to access host files and credentials available to the invoking user. It is restricted to the review benchmark, forbidden with --apply and whenever CI is set; promotion-capable and CI runs must use Bubblewrap.

Prompt and skill evolution loop

Prompts age as models and tool harnesses change. Treat the current skills and router thresholds as an incumbent policy, not permanent truth. Candidate changes run offline in the same throwaway clones as the incumbent; production skills never rewrite themselves from a live task.

Build an overlay that mirrors only the canonical repo-local skill paths:

/tmp/gn-skill-candidate/
└── .claude/skills/
    ├── gitnexus-plan/SKILL.md
    └── gitnexus-work/SKILL.md

The overlay may contain Markdown files from either of those two skill trees. The runner rejects every other path, including source, test, and MCP configuration files, so a candidate cannot improve its score by changing the task or verifier. Arm selection is derived from the touched skill and must be exact: a plan-only overlay runs the workflow pair; any work overlay runs both workflow and direct-work pairs. Subsets and unrelated extra pairs fail before paid work. For a work overlay:

cd eval
uv run --locked --extra dev python -m workflow_bench.runner \
  --tasks workflow_bench/tasks.scenarios.yaml \
  --runs 3 --workers 1 --model claude-sonnet-4-20250514 \
  --arms workflow candidate_workflow \
         workflow_direct candidate_workflow_direct \
  --candidate-overlay /tmp/gn-skill-candidate

Candidate runs start from the same task commit, then receive a clean ephemeral commit containing the overlay. results.jsonl records the named model, task commit, task-prompt digest, skill digest, overlay digest, hidden-oracle command/manifest/content digests, immutable dependency content/manifest digests, separate authored-test and oracle outcomes, timestamp, local session ids, and digest-bound parent-captured event-stream artifacts. Those artifacts are the trajectory evidence: cluster failures and expensive detours, propose one bounded prompt change, and feed it back as the next overlay.

When candidate arms are present the runner also writes schema-6 promotion.json. It binds the immutable overlay digest, benchmark model, truthful candidate origin (a named proposer model or manual-initial-overlay), selected task definitions, resolved commits, and exact hidden-oracle bytes/commands, immutable dependency bytes, committed base digest of every apply destination, exact required arms, thresholds, and evidence expiry. Its default deterministic gate is deliberately conservative:

Schema 6 binds a separate policy to each required candidate arm and records whether the sweep completed. Apply validates the paired metrics and recomputes each decision. Historical schema 5 reports remain readable; regenerate their benchmark evidence before applying an overlay. Editing a schema number does not supply the missing evidence.

Review candidates optimize weighted F1 with a minimum improvement of 0.01, complete paired evidence on every selected task, and no per-task quality regression. Complete misses score zero. Matching uses maximum cardinality throughout the 100-finding limit. Downgraded findings receive at most their reported severity's weight; only blocking-severity matches count toward blocker recall. Every valid candidate repeat must have the correct verdict, and the minimum blocker recall across repeats must not regress. Clean controls retain their false-positive and verdict safeguards. Implementation candidates retain the efficiency policy below:

• at least 3 paired VALID runs per task, zero excluded runs in either arm (session/infra-error rows therefore block promotion), and a named model;
• a fully measured task that neither arm ever resolves remains reported but is ungated from the quality comparison — only if its metric was measured in both arms and no run hit skill-not-invoked (a skill that never loaded is prompt evidence, not task health). An ungated task still ranks against a looser 100% failed-task regression cap on the promotion metric;
• at least half the paired tasks must stay gated, and promotion.json discloses the gated/ungated split per decision; a set with no gated task at all is insufficient_evidence;
• the candidate must pass the hidden oracle on every valid run of every gated task the incumbent resolves at least once — on a task the incumbent never resolves, partial candidate progress counts as improvement instead of failing the floor, so making some progress is never scored worse than making none;
• no per-task resolution-rate regression (quality is lexicographically first);
• promotion by resolution needs a margin of at least 2 resolved runs — a 1-run difference is noise at this run count and falls through to the efficiency comparison;
• with equal quality, at least 5% median improvement on the promotion metric (default cost_usd — the only CLI-reported number that includes subagent spend; token metrics count only the main-loop session and flatter subagent-heavy candidates, so selecting one stamps a warning into promotion.json);
• no individual task may regress the selected efficiency metric by more than 20%.

Tune the efficiency signal with --promotion-metric and the three --promotion-* thresholds. Applying requires one unique promote decision for every bound candidate arm. The driver then stages every canonical and shipped mirror, verifies that all destination bytes still match the bound bases, replaces them as one compare-and-swap set, verifies byte parity, and rolls every landed replacement back on failure or interruption. keep_incumbent and insufficient_evidence become the next learning queue; their raw results.jsonl rows carry the session_ids of the trajectories to inspect.

Re-run the paired suite whenever the named model or tool harness changes, and at least every 90 days otherwise. This is prompt-policy optimization using verified agent trajectories as reward evidence; it is intentionally not online model-weight RL. The same records can feed a later offline RL pipeline without weakening today's deterministic promotion boundary.

Closing the loop automatically (evolve.py)

The evolution workflow runs an offline containment preflight with the pinned Claude Code 2.1.214 binary before starting a paid proposer or benchmark. The review canary seals the workspace read-only and exposes only the pre-created review-output.json as writable. Runtime mount placeholders are prepared in the disposable clone before sealing it; existing config bytes are preserved. Any pre-existing result entry, including a symlink, is rejected. Required canaries fail when their runtime or Bubblewrap is unavailable.

The default outage limit is five consecutive unusable results, across task boundaries. Invalid review JSON advances this limit even when a skill or session error was recorded first. A valid zero-quality review resets it. Concurrent waves can exceed the limit by at most workers - 1 completed cells; no further wave starts after a trip. Completed rows and redacted diagnostics remain in the partial report, the runner exits nonzero, and the evolution driver stops without applying or starting another generation.

SIGINT and SIGTERM propagate one cancellation event through managed commands, including clone, setup, Claude, and verification. Executor submissions copy the run context so indirect subprocess helpers receive the same event. Active process groups or Windows Job Objects are terminated and workers joined before shared assets or the gateway are released. Controlled cancellation tests require cleanup within 15 seconds. Cancellation remains distinct from timeout and quality failure in recorded evidence.

The gateway runs under a private supervisor watching a pipe owned only by the harness. Parent exit, including SIGKILL, closes that pipe and stops the proxy group; Windows also retains kill-on-close Job Object ownership. Keep completed JSONL rows, transcripts, the partial report, and gateway diagnostics when investigating an interrupted run. A subsequent paid comparison needs fresh evidence from all arms under the same dependency lock. LiteLLM pricing comes from that locked release's local cost map; compare no old/new-lock costs as quality evidence.

workflow_bench.evolve automates the three manual arrows — propose, benchmark, apply — without moving the trust boundary:

cd eval
./workflow_bench/run-evolution.sh                  # local; no working-tree apply
./workflow_bench/run-evolution.sh --apply          # CI; same argv the workflow uses
./workflow_bench/run-evolution.sh --dry-run        # print the evolve command

The GitHub skill-evolution job calls this script. Do not invoke python -m workflow_bench.evolve directly for a full loop. Environment knobs match the workflow: MODEL, PROPOSER_MODEL, GENERATIONS, RUNS, WORKERS, PROVIDER, EFFORT, SEED_RESULTS, INCLUDE_EXPENSIVE. The checked-in production defaults are PROVIDER=openai, MODEL=gpt-5.6-sol, PROPOSER_MODEL=gpt-5.6-sol, and EFFORT=xhigh.

The scheduled/default profile is read-only review evolution. Set EVOLUTION_PROFILE=implementation explicitly to run the legacy plan/work benchmark. Review mode requires CE_PLUGIN_DIR and CE_PLUGIN_VERSION.

Each review generation: a confined proposer session reads only the incumbent gitnexus-review skill, normalized CE/incumbent/candidate result rows, bounded review artifacts and session transcripts, and the rejected proposal.md when available (including a workflow seed from a prior run), and the learning queue, then writes ONE bounded candidate overlay plus a reviewer-facing proposal.md. The proposer's clone is sanitized exactly like an arm's before its session starts: it authors the artifact the arms are scored with, so letting it read eval/workflow_bench would hand it the task prompts and the hidden oracles it is about to be graded against, and a proposal could win the gate by encoding the expected behavior into a skill rather than by being a better skill. The overlay is re-validated by candidate_overlay_files (same boundary: Markdown under gitnexus-review, including exercised ci-personas/, nothing else), frozen, and exercised only by its exact required pairs. Task refs are resolved once before generation zero and the immutable task bindings are forwarded to every generated runner invocation, so a moving branch cannot change later evidence. The deterministic quality-first gate rejects blocker-recall regressions, new false positives on clean controls, and any weighted-score regression. Repeated evidence (RUNS>=3) is required for promotion; RUNS=1 is diagnostic-only. CE is the external comparator. Cost and latency are tiebreakers and never compensate for quality loss. promote stops the loop; with --apply the authorized frozen bytes are transactionally applied to the canonical .claude/skills/ trees and their shipped mirrors as an ordinary working-tree diff — committing, CI (shipped-skills-sync, skills-steering), and the PR merge stay human. keep_incumbent feeds that generation's trajectories to the next proposer. --initial-overlay skips the generation-0 proposer to benchmark a hand-written candidate; --proposer-model upgrades only the diagnosis session.

Learning queue. Live skill runs never self-edit (see each skill's "Skill feedback" section) — instead they may append one-line JSON notes to workflow_bench/learnings.jsonl (gitignored, machine-local like the transcripts they complement). The proposer reads the queue as hints, not ground truth: a learning only reaches a shipped skill by surviving the same paired benchmark as any other candidate.

For ad-hoc use, run the driver on the existing re-evaluation triggers (model/harness change or 90-day staleness). The repository workflow runs a deliberate weekly drift check: scheduled concurrency stays serial unless GITNEXUS_EVOLUTION_WORKERS is raised after a funded host-sized proof, and --workers is bounded to 1–8 before paid work starts. --generations remains the only loop bound.

Free-model setup (no paid tokens)

Headless Claude Code honors ANTHROPIC_BASE_URL, and litellm (already an eval dependency) can proxy its Anthropic-compatible /v1/messages to a model that costs nothing — a hosted OpenRouter :free variant or a fully local Ollama model. Config template: free-model.litellm.yaml.

# 1. Choose a proxy master key and start the proxy
#    (pick/edit a model route in the yaml first; keep the proxy on loopback —
#    anyone who can reach the port with this key can spend the backend quota)
export LITELLM_MASTER_KEY="$(openssl rand -hex 16)"
uv run --locked --with 'litellm[proxy]' litellm --config workflow_bench/free-model.litellm.yaml --port 4000

# 2. Point the benchmark at it
uv run --locked --extra dev python -m workflow_bench.runner \
  --tasks workflow_bench/tasks.scenarios.yaml --runs 3 \
  --base-url http://localhost:4000 --anthropic-api-key "$LITELLM_MASTER_KEY" --model free-coder

OpenAI API keys

Claude Code still speaks Anthropic /v1/messages. For a paid OpenAI backend, do not point --anthropic-api-key at an sk-... OpenAI key. Export the OpenAI key and use OpenAI model ids; the driver starts the proxy itself:

export GITNEXUS_BENCH_OPENAI_API_KEY="$OPENAI_API_KEY"
PROVIDER=openai ./workflow_bench/run-evolution.sh

The GitHub skill-evolution workflow accepts GITNEXUS_BENCH_OPENAI_API_KEY on the gitnexus-evolution environment. Dispatch with provider=openai to force that backend even when an Anthropic token is also configured (otherwise auto keeps using Anthropic whenever that secret exists). Claude default model inputs are then rewritten to gpt-5.6-sol; every proposer and benchmark session receives --effort xhigh.

Caveats, honestly:

• Both arms run on the same model, so the comparison stays fair at any quality level — but small free models follow skills less reliably, so expect lower resolve rates and noisier savings than on frontier models. Treat free-model runs as directional; confirm headline numbers with a small paid run.
• Through a proxy cost_usd reads ~0, and the CLI's token counts are NOT a substitute "real metric": they cover only the main-loop session, so subagent spend is invisible to both. For efficiency ranking, prefer a paid run gated on cost_usd, or sum per-session usage from the transcripts (~/.claude/projects/<cwd-slug>/<session_id>.jsonl, deduplicating events that share one message.id).
• OpenRouter :free variants are rate-limited (~50 req/day on a fresh account); local Ollama has no limits.
• Codex users: codex exec --oss runs local models for free too, but this runner is Claude-Code-first; a codex engine is a straightforward extension (parse its --json usage events).

Historical ground base (2026-07-11, Claude Code 2.1.207, unnamed model, n=1/cell)

These figures predate mandatory model provenance and are retained only as historical calibration. They are not eligible promotion evidence and must not be combined with current named-model runs.

Three task classes × three arms, single-repo (GitNexus itself). Every arm resolved every task — at this difficulty, pass/fail quality is saturated and the comparison is pure cost:

task (class)	arm	resolved	cost $	wall	turns	vs baseline cost
trivial-version-alias	workflow	1/1	9.16	16m	63	−333%
trivial-version-alias	baseline	1/1	2.11	2.8m	16	—
inv-bug-pdg-note	workflow	1/1	14.56	21m	83	−331%
inv-bug-pdg-note	workflow_direct	1/1	5.23	7.5m	32	−55%
inv-bug-pdg-note	baseline	1/1	3.38	4.7m	22	—
inv-feature-list-repos-filter	workflow	1/1	13.22	19m	84	−211%
inv-feature-list-repos-filter	workflow_direct	1/1	4.87	4.8m	38	−15% (wall +14% faster)
inv-feature-list-repos-filter	baseline	1/1	4.25	5.5m	32	—

What the ground base says, honestly:

• The full plan→work workflow never paid for itself at this task scale (tasks a baseline agent finishes in ≤35 turns). Its fixed cost — freshness gate incl. analyzer rebuild + re-index, a full 13-section plan, work-phase re-anchoring — is ~$9–11 per task and needs much larger tasks, plan-reuse (one plan, several executors/sessions), or plan-as-deliverable flows to amortize.
• workflow_direct is close to baseline (−15% to −55% cost, once slightly faster wall) — the execution discipline (impact-before-edit, detect_changes-before-commit) is cheap. It produced noticeably more test coverage than baseline for near-equal cost on the feature task.
• Quality didn't differentiate because nothing failed. The regime where the workflow should win on resolve rate — cross-module tasks where baselines flail — is the unmeasured cell (cross-module-parse-retry), and the next thing to measure, ideally with --runs 3+ on a free backend.
• Caveats: n=1 per cell, one repo, one model; churn numbers from this run predate the intent-to-add/exclude-plans churn fix, so they are not comparable across arms and are omitted above.

Routing implication (to revisit as cells fill in): for tasks up to this size, gitnexus-work direct mode or a plain agent is the cost-optimal route; reserve full gitnexus-plan → gitnexus-work for cross-module work, multi-session execution, or when the plan document itself is a deliverable. If a future run shows the workflow flattering itself here, distrust the run.

Cross-module cell (same day, optimized skills, n=1)

The hardest class — retry-with-backoff across the worker-pool/pipeline seams, transient-vs-deterministic classification:

arm	resolved	cost $	wall	turns	churn
workflow	1/1	18.32	37m	107	4/+373/−17
workflow_direct	1/1	9.53	15m	52	11/+244/−66
baseline	1/1	18.03	34m	98	6/+345/−69

(The workflow_direct row is the clean re-run under clone isolation — the original was contaminated, see the integrity note below.)

This is the cell where the discipline pays. workflow_direct — the execution skill without a planning pass — beat a plain agent by 47% cost and 56% wall time on the hardest class while resolving: impact-first navigation and gated commits prevented the flailing that baseline's 98 turns represent. The full workflow's premium vanished (−1.6% vs baseline; −211%..−333% on smaller classes) — fixed costs amortize here, with a less destructive diff and a durable plan artifact — but it didn't beat direct mode on any measured axis with the plan consumed only once. Resolve rate stayed tied across all cells; the savings story belongs to the execution discipline, and the planning pass is bought for its artifact (multi-session reuse, review, handoff), not for same-session token savings.

Benchmark integrity note (why churn earns its keep): the original workflow_direct cell reported an impossible 28-turn/$4.71 solve with churn byte-identical to the workflow arm — because git worktree add shares the ref namespace, the workflow arm's slug branch survived worktree removal, and the direct arm found and adopted the finished work. Fixed by giving every arm an isolated git clone --no-local --no-hardlinks with no object alternates (agent-created refs and storage die with the clone); the leaked branch was deleted and the cell re-measured. Treat identical churn fingerprints across arms as a contamination alarm.

Optimization re-measurement (same day, commit 830a0459)

After category-priced plan forms (compact ≤80 lines + mini-pack), category-priced freshness (accept for compact classes), per-category turn budgets, and the work-phase HEAD==pin fast path, the same inv-bug-pdg-note workflow cell re-measured (n=1):

	ground base	optimized	delta
resolved	✅	✅	—
cost $	14.56	11.70	−20%
turns	83	72	−13%
output tokens	59,789	53,345	−11%
cache_read	6.64M	5.07M	−24%
wall	21m	25m	+15%

Verified in-transcript: the compact form fired (115-line plan vs 209 for a simpler task pre-optimization), the plan session dropped 72→49 turns, and NO analyzer rebuild/re-index executed. All savings came from the plan side; this run's work session drew a long test-debugging tail (hence the wall regression) — single-run variance cuts both ways. The optimizations narrow the gap but do not flip the regime: the workflow remains ~3.5× baseline on this task class, so the routing rule above stands unchanged.

Writing good tasks

See tasks.scenarios.yaml. Small enough to finish headless, real enough to require investigation — the workflow's savings come from not re-reading and not re-investigating, which trivial tasks never exercise. Keep verify as a model-visible authored-test quality signal, and add an independent oracle whose source files live under workflow_bench/oracles/. Oracle commands must run only files staged beneath $GITNEXUS_BENCH_ORACLE_ROOT; for Vitest, include the shared vitest.config.mts as an oracle file and pass it explicitly with --config. Prefer verify commands that use the repo's own npm scripts (they carry build pre-hooks).

Relation to the SWE-bench harness

The rest of eval/ benchmarks GitNexus tools inside a litellm agent loop (baseline vs graph-enhanced). This module benchmarks the skill workflow inside the real CLI harness those skills ship for. Different question, same spirit: measure, don't assume.