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skill-optimizer

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Mis à jour26 mars 2026 à 14:08

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.

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

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Source

mcollina

mcollina/skills

Ouvrir le dépôt GitHub Voir les dépôts du créateur

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Autres occupations informatiquesProfessions informatiques et mathématiques·SOC 15-1299

Explorateur de fichiers

7 fichiers

SKILL.md

readonly

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)

Measure baseline and skill-on behavior (per model, per scenario, per criterion)
Find failure pattern:
- universal failure (0% with skill)
- model-specific weakness
- regression (negative delta)
Edit for salience:
- add explicit triggers
- add concrete integrated examples
- tighten checklists and decision rules
Re-run evals and compare deltas
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

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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)

Measure baseline and skill-on behavior (per model, per scenario, per criterion)
Find failure pattern:
- universal failure (0% with skill)
- model-specific weakness
- regression (negative delta)
Edit for salience:
- add explicit triggers
- add concrete integrated examples
- tighten checklists and decision rules
Re-run evals and compare deltas
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