skill-optimizer

Original：🇺🇸 English
Translated

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.

3installs
Sourcemcollina/skills
Added on

NPX Install

npx skill4agent add mcollina/skills skill-optimizer

Tags

Translated version includes tags in frontmatter
ai-skill-optimizationcross-model-reliabilitybenchmark-testingregression-preventioncontext-cost-reduction

SKILL.md Content

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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