GitNexus/eval/workflow_bench/README.md

# 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

```bash
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:

```bash
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:

```bash
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 `copytree`s 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:

```text
/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:

```bash
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:

```bash
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`.

```bash
# 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:

```bash
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.