Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/hyhmrright/logic-lensnpx agentmods add skills/hyhmrright/logic-lens/run-iteration-evalWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hyhmrright/logic-lens/run-iteration-eval)<a href="https://agentmods.dev/skills/hyhmrright/logic-lens/run-iteration-eval"><img src="https://agentmods.dev/badge/skills/hyhmrright/logic-lens/run-iteration-eval/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hyhmrright/logic-lens/run-iteration-eval"><img src="https://agentmods.dev/badge/skills/hyhmrright/logic-lens/run-iteration-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00108 | $0.00759 |
| Opus 5 | $0.00054 | $0.00380 |
| Sonnet 5 | $0.00022 | $0.00152 |
| Haiku 4.5 | $0.00011 | $0.00076 |
Grade A, and why
run-iteration-eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 thesync-skill-cacheskill (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 SHAThe runner is idempotent — a case with an existing
output.mdis skipped. Delete theeval-<id>/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. Thelogicsubscore reflects reasoning quality;formatreflects 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>
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 56 lines · 108 tokens per session scan A 0755688051d7
run-iteration-eval is a skill published in the GitHub repository hyhmrright/logic-lens (22 stars, last pushed 11d ago), licensed MIT. It adds 108 tokens to every session and 759 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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