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run-iteration-eval

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Mis à jour1 juin 2026 à 11:17

Run the Logic-Lens content-eval pipeline for one iteration and produce a scored summary.json — use to measure a skill change. Wraps scripts/run-content-evals.sh (runner, costs tokens) and scripts/grade-iteration.py (grader, free, re-runnable). ALWAYS sync the plugin cache first. Use when the user wants to "run the evals", "score this iteration", "measure the skill change", "smoke-test before the full run", or "re-grade existing outputs".

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

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Source

hyhmrright

hyhmrright/logic-lens

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

readonly

name	run-iteration-eval
description	Run the Logic-Lens content-eval pipeline for one iteration and produce a scored summary.json — use to measure a skill change. Wraps scripts/run-content-evals.sh (runner, costs tokens) and scripts/grade-iteration.py (grader, free, re-runnable). ALWAYS sync the plugin cache first. Use when the user wants to "run the evals", "score this iteration", "measure the skill change", "smoke-test before the full run", or "re-grade existing outputs".
disable-model-invocation	true

run-iteration-eval

Measures a skill change by running the content cases in evals/content/v2/evals-v2.json through claude -p and grading the outputs. Outputs land in skills-workspace/iteration-<TAG>/.

The runner and grader are split on purpose: running calls Claude and costs tokens; grading is pure regex Python and is free to re-run on outputs that already exist. Never re-run the runner just to re-score — re-grade instead.

Steps

Sync the cache first — non-negotiable. The runner loads the skill from the plugin cache, not skills/. Run the sync-skill-cache skill (or its script directly). If you skip this, the eval grades the previously-published skill and the entire run is wasted:
```
bash .claude/skills/sync-skill-cache/scripts/sync-cache.sh
```

Pick a scope. Full runs cost real tokens; scope down while iterating:

SMOKE=1 bash scripts/run-content-evals.sh              # one case per mode (~$0.10) — fast sanity
CASES="200 201 202" bash scripts/run-content-evals.sh  # only the cases a diagnosis flagged
TAG=myfix bash scripts/run-content-evals.sh            # full run, named tag
bash scripts/run-content-evals.sh                      # full run, tag = git short SHA

The runner is idempotent — a case with an existing output.md is skipped. Delete the eval-<id>/ dir to force a re-run of that case.

Read summary.json in the iteration dir. It carries overall pass rate plus the per-mode and per-subscore (logic vs format) breakdown. The logic subscore reflects reasoning quality; format reflects Output-Skeleton compliance and is the historical bottleneck with high single-run variance. Judge a change on the right subscore — a format wobble is not a reasoning regression.
Re-grade without re-running (free) after editing the grader or to recompute on existing outputs:
```
python3 scripts/grade-iteration.py skills-workspace/iteration-<TAG>
```
Single-iteration grade without the full suite — when you already have outputs and only want the score table, grade-iteration.py <iteration-dir> is the cheapest path (see scripts/README.md).

Variance caveat

logic-review single-run scores are variance-dominated (see project memory). One run is a signal, not a verdict — for a decision near the margin, run the affected cases 2–3× or widen the case set before concluding a change helped or hurt. Hand the result to iteration-guard for the ship/rollback call rather than eyeballing a single number.

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Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy). Trigger when a user shares code and suspects something is wrong without naming a concrete failure — phrases like "review this", "does this look right", "check this function", "audit this code", "tests pass but prod fails". SCOPE HARD RULE: one file or one function only. For a directory or whole module use logic-health; for a confirmed failure (stack trace, failing test, specific wrong value) use logic-locate; for two versions use logic-diff; for repo-wide autonomous fixing use logic-fix-all. Do NOT trigger for: style/formatting, security scanning, performance, test generation, architecture or design questions.

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2026-05-2811

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Scaffold a new logic-* skill in the Logic-Lens repo and wire it into every place a skill must be registered, so no step is missed. Use when adding a seventh (or later) skill to Logic-Lens.

2026-05-2811

logic-diff

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Compare two code versions for semantic equivalence via semi-formal tracing of both versions side-by-side. Trigger when the user shares a refactor, rewrite, migration, or A/B implementation and wants to confirm behavior is unchanged — "did I break anything", "is this equivalent", "are these equivalent", "semantically equivalent", "are these two implementations semantically equivalent", "check my refactor", "same behavior after the change?", "does my rewrite produce the same output", "switched from X to Y — same results?". SCOPE HARD RULE: requires two code versions (A and B). A single version for bug-finding uses logic-review; one version + a failing test uses logic-locate; explaining what one piece of code does uses logic-explain; codebase audit uses logic-health. Do NOT trigger for: single-version review, performance comparison, design-quality comparison, or "which is better-written" questions.

2026-05-1511

name	run-iteration-eval
description	Run the Logic-Lens content-eval pipeline for one iteration and produce a scored summary.json — use to measure a skill change. Wraps scripts/run-content-evals.sh (runner, costs tokens) and scripts/grade-iteration.py (grader, free, re-runnable). ALWAYS sync the plugin cache first. Use when the user wants to "run the evals", "score this iteration", "measure the skill change", "smoke-test before the full run", or "re-grade existing outputs".
disable-model-invocation	true

run-iteration-eval

Measures a skill change by running the content cases in evals/content/v2/evals-v2.json through claude -p and grading the outputs. Outputs land in skills-workspace/iteration-<TAG>/.

Steps

Sync the cache first — non-negotiable. The runner loads the skill from the plugin cache, not skills/. Run the sync-skill-cache skill (or its script directly). If you skip this, the eval grades the previously-published skill and the entire run is wasted:
```
bash .claude/skills/sync-skill-cache/scripts/sync-cache.sh
```

Pick a scope. Full runs cost real tokens; scope down while iterating:

SMOKE=1 bash scripts/run-content-evals.sh              # one case per mode (~$0.10) — fast sanity
CASES="200 201 202" bash scripts/run-content-evals.sh  # only the cases a diagnosis flagged
TAG=myfix bash scripts/run-content-evals.sh            # full run, named tag
bash scripts/run-content-evals.sh                      # full run, tag = git short SHA

The runner is idempotent — a case with an existing output.md is skipped. Delete the eval-<id>/ dir to force a re-run of that case.

Read summary.json in the iteration dir. It carries overall pass rate plus the per-mode and per-subscore (logic vs format) breakdown. The logic subscore reflects reasoning quality; format reflects Output-Skeleton compliance and is the historical bottleneck with high single-run variance. Judge a change on the right subscore — a format wobble is not a reasoning regression.
Re-grade without re-running (free) after editing the grader or to recompute on existing outputs:
```
python3 scripts/grade-iteration.py skills-workspace/iteration-<TAG>
```
Single-iteration grade without the full suite — when you already have outputs and only want the score table, grade-iteration.py <iteration-dir> is the cheapest path (see scripts/README.md).