| name | skill-optimizer |
| description | Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, improving examples, shrinking context cost, or setting benchmark/release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience. |
| metadata | {"tags":"skills, optimization, benchmarking, activation, regressions, prompt-engineering"} |
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:
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
