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