Run Iteration Eval
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…
Install
$ agentstack add skill-hyhmrright-logic-lens-run-iteration-eval ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-hyhmrright-logic-lens-run-iteration-eval) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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-/.
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 bash .claude/skills/sync-skill-cache/scripts/sync-cache.sh ``
- 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-/` dir to force a re-run of that case.
- Read
summary.jsonin 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: ``bash python3 scripts/grade-iteration.py skills-workspace/iteration- ``
- Single-iteration grade without the full suite — when you already have outputs and only want
the score table, grade-iteration.py 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.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: hyhmrright
- Source: hyhmrright/logic-lens
- License: MIT
- Homepage: https://github.com/hyhmrright/logic-lens
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.