skill-optimizer

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)

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 - 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