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run-iteration-eval

run-iteration-eval is a skill for Claude Code from hyhmrright/logic-lens. It costs 108 tokens per session (759 once invoked), scanned A, original, MIT.

A procedure for running one iteration of the Logic-Lens content-evaluation pipeline and producing a scored summary file.

In plain words
What is it for?
Use it to run smoke tests or selected cases, grade outputs, and save results under a named iteration directory.
Why use it?
It ensures the evaluation uses the current cached skill and avoids spending new model calls just to re-score existing results.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: reads .claude/ paths; positional $N argument.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is bash .claude/skills/sync-skill-cache/scripts/sync-cache.sh.

Part of the logic-lens plugin — 11 skills, 6 commands, 3 agents, 2 hooks shipped together

not rated 25repo 25d ago A scan SkillSpector: pass 108 tokens original MIT

Good fit Use it to run smoke tests or selected cases, grade outputs, and save results under a named iteration directory.

Compare 6 skills from other repositories ↓
View source ↗ hyhmrright/logic-lens
Install

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.

Clone the repo
git clone --depth 1 https://github.com/hyhmrright/logic-lens
agentmods
npx agentmods add skills/hyhmrright/logic-lens/run-iteration-eval

Made for: Claude Code.

Or install logic-lens, the plugin that ships this one along with the rest of its 11 skills, 6 commands, 3 agents, 2 hooks.

Wrote 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.

agentmods badge for run-iteration-eval

README.md
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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.

agentmods 80×15 button for run-iteration-eval

Your own site · 80×15
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Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 759 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00108 $0.00759
Opus 5.5 $0.00043 $0.00304
Sonnet 5.5 $0.00022 $0.00152
Haiku 4.5 $0.00011 $0.00076

Measured 1mo ago against content hash 0755688051d7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo 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.

.claude/skills/run-iteration-eval/SKILL.md · 56 lines

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

  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 .claude/skills/sync-skill-cache/scripts/sync-cache.sh

  2. 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.

  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:

    python3 scripts/grade-iteration.py skills-workspace/iteration-<TAG>

Read the full file on GitHub · 56 lines

Changes

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.

  1. 1mo ago First seen · 56 lines · 108 tokens per session scan A 0755688051d7

Subscribe to this mod's changes

run-iteration-eval is a skill published in the GitHub repository hyhmrright/logic-lens (25 stars, last pushed 25d ago), licensed MIT. It adds 108 tokens to every session and 759 once invoked, about $0.0004 per session on Opus 5.5. 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.

Code reviewTestingai-code-reviewbug detectionclaude-code-plugincode-qualitygemini-cli-extension
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