skill-optimizer

When to use

• 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

• 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