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skill-optimizer

النجوم١٬٨٥٦

التفرعات١٥٠

آخر تحديث٢٦ مارس ٢٠٢٦ في ١٤:٠٨

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.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

mcollina

mcollina/skills

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

المهن الحاسوبية الأخرىمهن الحاسوب والرياضيات·SOC 15-1299

مستكشف الملفات

7 ملفات

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

المزيد من هذا المستودع

نفس المستودع

octocat

mcollina/skills

Use this skill whenever the prompt contains any `github.com` URL, even if the user only pastes a link and gives no GitHub-specific keywords. Handles git and GitHub operations using the gh CLI. Triggers include any GitHub link to an issue, pull request, commit, compare page, Actions run, release, discussion, or repository. Covers creating and reviewing PRs, watching CI checks, interactive rebasing, branch cleanup, submodule management, and repository archaeology with git log/blame/bisect.

2026-04-171.9k

nodejs-core

mcollina/skills

Debugs native module crashes, optimizes V8 performance, configures node-gyp builds, writes N-API/node-addon-api bindings, and diagnoses libuv event loop issues in Node.js. Use when working with C++ addons, native modules, binding.gyp, node-gyp errors, segfaults, memory leaks in native code, V8 optimization/deoptimization, libuv thread pool tuning, N-API or NAN bindings, build system failures, or any Node.js internals below the JavaScript layer.

2026-03-281.9k

node

mcollina/skills

Provides domain-specific best practices for Node.js development with TypeScript, covering type stripping, async patterns, error handling, streams, modules, testing, performance, caching, logging, and more. Use when setting up Node.js projects with native TypeScript support, configuring type stripping (--experimental-strip-types), writing Node 22+ TypeScript without a build step, or when the user mentions 'native TypeScript in Node', 'strip types', 'Node 22 TypeScript', '.ts files without compilation', 'ts-node alternative', or needs guidance on error handling, graceful shutdown, flaky tests, profiling, or environment configuration in Node.js. Helps configure tsconfig.json for type stripping, set up package.json scripts, handle module resolution and import extensions, and apply robust patterns across the full Node.js stack.

2026-03-131.9k

documentation

mcollina/skills

Creates, structures, and reviews technical documentation following the Diátaxis framework (tutorials, how-to guides, reference, and explanation pages). Use when a user needs to write or reorganize docs, structure a tutorial vs. a how-to guide, build reference docs or API documentation, create explanation pages, choose between Diátaxis documentation types, or improve existing documentation structure. Trigger terms include: documentation structure, Diátaxis, tutorials vs how-to guides, organize docs, user guide, reference docs, technical writing.

2026-03-131.9k

fastify-best-practices

mcollina/skills

Guides development of Fastify Node.js backend servers and REST APIs using TypeScript or JavaScript. Use when building, configuring, or debugging a Fastify application — including defining routes, implementing plugins, setting up JSON Schema validation, handling errors, optimising performance, managing authentication, configuring CORS and security headers, integrating databases, working with WebSockets, and deploying to production. Covers the full Fastify request lifecycle (hooks, serialization, logging with Pino) and TypeScript integration via strip types. Trigger terms: Fastify, Node.js server, REST API, API routes, backend framework, fastify.config, server.ts, app.ts.

2026-03-131.9k

init

mcollina/skills

Creates, updates, or optimizes an AGENTS.md file for a repository with minimal, high-signal instructions covering non-discoverable coding conventions, tooling quirks, workflow preferences, and project-specific rules that agents cannot infer from reading the codebase. Use when setting up agent instructions or Claude configuration for a new repository, when an existing AGENTS.md is too long, generic, or stale, when agents repeatedly make avoidable mistakes, or when repository workflows have changed and the agent configuration needs pruning. Applies a discoverability filter—omitting anything Claude can learn from README, code, config, or directory structure—and a quality gate to verify each line remains accurate and operationally significant.

2026-03-131.9k

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