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) 1. **Measure baseline and skill-on behavior** (per model, per scenario, per criterion) 2. **Find failure pattern**: - universal failure (0% with skill) - model-specific weakness - regression (negative delta) 3. **Edit for salience**: - add explicit triggers - add concrete integrated examples - tighten checklists and decision rules 4. **Re-run evals** and compare deltas 5. **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](rules/benchmark-loop.md) - End-to-end benchmark loop and scoring - [rules/activation-design.md](rules/activation-design.md) - Improve retrieval and instruction uptake - [rules/context-budget.md](rules/context-budget.md) - Reduce token cost without losing behavior - [rules/regression-triage.md](rules/regression-triage.md) - Diagnose and fix skill-on regressions - [rules/release-gates.md](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