A Claude Code plugin that puts a skill to work, scores it across three levels, and hands back a concrete list of improvements — without ever touching the skill it is testing.
Writing a skill is easy. Knowing whether it actually helps is not. skill-iterator answers that question empirically:
- Run it for real — make the skill perform an actual task, not a hypothetical one.
- Score in three layers — L1: did it produce anything? · L2: is the output correct? · L3: is it any good?
- Run every task twice — once with the skill, once without — so you can isolate what the skill itself contributed.
- Get an actionable scorecard — every suggestion names a file, a change, and a reason. No filler.
The plugin splits by user intent — pick the one that matches what you want to know:
/skill-iterator— umbrella router; start here if you are not sure which flow you need./iterate-skill— L1/L2/L3 content scoring + improvement suggestions over multiple iterations./trigger-tune— optimize a skill's description so it fires at the right time (trigger-rate tuning)./blind-test— end-to-end output test with anti-fixture-leak guards.
- Read-only on the target. It never mutates the skill under test — it only emits a
suggestions.json. Applying changes is a separate, deliberate step. - With / without double-run. The core invariant: you cannot claim a skill helps unless you have measured the same task without it.
- Three-layer lint. Schema and structure are validated by a PostToolUse hook, a git pre-commit gate, and a manual validator.
This repo is a Claude Code marketplace containing one plugin. From inside Claude Code:
# 1. add the marketplace
/plugin marketplace add synergy321/skill-iterator
# 2. install the plugin
/plugin install skill-iterator@skill-iterator-plugin
| plugin | skill-iterator |
| marketplace | skill-iterator-plugin |
Skills, agents, and the lint hook are auto-discovered — no extra configuration.
- Read-only evaluation works out of the box. Scoring, blind-test, and the suggestion scorecard run with just Claude Code + Python 3.
- Applying suggestions expects an external editing workflow. Several skills route the "apply changes" step to
skill-creator, an authoring tool that is not bundled with this plugin. Without it you still get every finding — you just apply the edits with your own workflow. - Batch mode needs an API key.
run_loop.pydrives the Anthropic SDK directly and readsANTHROPIC_API_KEYfrom the environment; the agent-spawn path does not. - Skill prompts and output are in Chinese. The evaluation methodology is language-agnostic, but the user-facing text is Chinese.
MIT © 2026 Travis Chong. Use it, fork it, ship it.