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run-iteration-eval

Price unconfirmed★ 24 GitHub starsRegistry updated · Sep 30, 2026agent-skill

Overview

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".

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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.

View source ↗

File metadata

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

View original text

---
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

1. **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
bash .claude/skills/sync-skill-cache/scripts/sync-cache.sh
```

2. **Pick a scope.** Full runs cost real tokens; scope down while iterating:
```bash
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.

3. **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.

4. **Re-grade without re-running** (free) after editing the grader or to recompute on existing
outputs:
```bash
python3 scripts/grade-iteration.py skills-workspace/iteration-<TAG>
```

5. **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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License: MIT

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Quality score needs review
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GitHub adoption: 24 GitHub stars
Stars/forks activity: 24 stars, 2 forks; issue activity unavailable in current metadata
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Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: hyhmrright/logic-lens
License: MIT
Version: Unknown
Last GitHub push: Sep 30, 2026
Registry updated: Sep 30, 2026
Instruction path: .claude/skills/run-iteration-eval/SKILL.md @ bcf8dbd3f3a5

Version reported in registry metadata; check source releases before relying on it.

Quality

55/100

Promising

Trust

58/100

Do not auto-install

Audit

71/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 24 GitHub stars
Stars/forks activity: 24 stars, 2 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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More details

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run-iteration-eval

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Price unconfirmed★ 24 GitHub starsRegistry updated · Sep 30, 2026agent-skill

Overview

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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).

Variance caveat

View source ↗

File metadata

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

View original text

# 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

4. **Re-grade without re-running** (free) after editing the grader or to recompute on existing
outputs:
```bash
python3 scripts/grade-iteration.py skills-workspace/iteration-<TAG>
```

## 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.

Review the source

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Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
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AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 24 GitHub stars
Stars/forks activity: 24 stars, 2 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Source & usage notes

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Source repository: hyhmrright/logic-lens
License: MIT
Version: Unknown
Last GitHub push: Sep 30, 2026
Registry updated: Sep 30, 2026
Instruction path: .claude/skills/run-iteration-eval/SKILL.md @ bcf8dbd3f3a5

Version reported in registry metadata; check source releases before relying on it.

Quality

55/100

Promising

Trust

58/100

Do not auto-install

Audit

71/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 24 GitHub stars
Stars/forks activity: 24 stars, 2 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Source: hyhmrright/logic-lens
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