/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](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