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run-iteration-eval

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Actualizado1 de junio de 2026 a las 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".

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

hyhmrright

hyhmrright/logic-lens

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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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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).