skill-optimizer

When to use Use this skill when you need to: Improve whether a skill is actually applied by models Diagnose why some criteria fail across all models Prevent a skill from making outputs worse Refactor skill text for stronger retrieval under context pressure Build repeatable benchmark loops and release gates Optimization loop (default workflow) Measure baseline and skill-on behavior (per model, per scenario, per criterion) Find failure pattern : universal failure (0% with skill) model-specific weakness regression (negative delta) Edit for salience : add explicit triggers add concrete integrated examples tighten checklists and decision rules Re-run evals and compare deltas Ship with guardrails (documented gate + run history + follow-up issues) How to use Read individual rule files for detailed procedures and templates: rules/benchmark-loop.md - End-to-end benchmark loop and scoring rules/activation-design.md - Improve retrieval and instruction uptake rules/context-budget.md - Reduce token cost without losing behavior rules/regression-triage.md - Diagnose and fix skill-on regressions rules/release-gates.md - Go/no-go criteria before shipping skill updates Practical heuristics Prefer few high-signal rules over many soft recommendations Put fragile, high-value behaviors in top-level checklists Include at least one integrated example per common scenario Add explicit wording for what must not be omitted Track gains/losses with with-skill vs without-skill comparisons