Agent skill · operations · 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/release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience.
Why this skill is useful
Provides domain-specific knowledge on optimizing AI skill performance and benchmarking that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-03. 1,894 stars on the source repository.
What this skill does
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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills mcollina/skill-optimizer