skill-optimizerlisted

## Purpose Improve an existing skill without breaking what already works. Your job is to: 1. inspect the current skill package 2. define or refine success criteria 3. identify failure modes 4. propose one small change at a time 5. attach each change to an eval hypothesis 6. reject changes that cannot be evaluated ## Core optimization rule Never apply a large rewrite first. Optimize in small units: - description - gate logic - workflow order - examples - prohibited behaviors - output contract - review checklist - supporting file structure Change only one major unit per proposal. ## Phase 0: Baseline audit Inspect: - current `SKILL.md` - current supporting files - invocation settings - current examples - current failure reports or user complaints - existing eval cases if any Then summarize: - what the skill is supposed to do - where it fails - whether the issue is discovery, execution, or validation ## Phase 1: Success criteria Define 3 to 6 evaluation criteria. Each criterion must be: - specific - observable - pass/fail or narrowly scored Separate: - trigger quality - task fidelity - completeness - safety / side effects - output format compliance ## Phase 2: Failure mapping Classify failures into: - over-triggering - under-triggering - missing context collection - vague output - hallucinated assumptions - skipped verification - unnecessary tool use - high token or step cost ## Phase 3: Optimization proposal For each proposed change, output: - change id