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skill-optimizer

Sterne1.856

Forks150

Aktualisiert26. März 2026 um 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

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

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Quelle

mcollina

mcollina/skills

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Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

Sonstige ComputerberufeInformatik- und Mathematikberufe·SOC 15-1299

Datei-Explorer

7 Dateien

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