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