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mcollina/skill-optimizer

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 and release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience.

87

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87%

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Overview
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SKILL.md

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

SKILL.md

tile.json