381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

search
expand_more
Active:
javierhbr
Showing 12 of 23 skills
javierhbr

noony-dependency-injection

by javierhbr
star 0

Use when resolving services with getService(), managing ContainerPool scopes (global vs local), configuring DependencyInjectionMiddleware, understanding the hybrid proxy container memory model, or accessing type-safe services in Noony controllers. Covers service RESOLUTION, not initialization.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-validation-schemas

by javierhbr
star 0

Use when validating request bodies with Zod schemas, understanding parsedBody vs validatedBody, ordering BodyParserMiddleware before BodyValidationMiddleware, handling Pub/Sub message validation, defining async validation, or inferring TypeScript types from Zod schemas in Noony handlers.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-complete-dual-entry

by javierhbr
star 0

The PRODUCTION PATTERN for Noony projects. Use when building a new project or graduating from `noony-create-fastify-server`. Complete dual-entry setup with Fastify (local dev) and Cloud Functions (production) working from the same handler code.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-convert-cloud-functions-to-fastify

by javierhbr
star 0

Use when MIGRATING existing Cloud Functions handlers to run locally with Fastify. Converts tightly-coupled Cloud Functions code into framework-agnostic handlers with dual deployment.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-create-fastify-server

by javierhbr
star 0

Use when setting up a Fastify server for local development from scratch. This is the STARTING POINT — get a dev server running fast, then graduate to `noony-complete-dual-entry` for production dual-entry.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-custom-adapter

by javierhbr
star 0

Use ONLY when creating adapters for unsupported frameworks like Koa, Hapi, or NestJS. Fastify, Express, and Cloud Functions are already built-in — you do not need this skill for those.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-dependency-initialization

by javierhbr
star 0

Use when initializing database connections, setting up singleton services, implementing the three-condition guard for concurrent-safe one-time initialization, connecting to external APIs at startup, or preventing duplicate DB connections during cold starts in Noony handlers.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-error-handling

by javierhbr
star 0

Use when throwing errors, handling error types, mapping errors to HTTP status codes, wrapping external API errors, or implementing custom error classes in Noony handlers. Covers the full error class hierarchy, cause chaining, ErrorHandlerMiddleware lifecycle, and ServiceError for non-HTTP contexts.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-guard-system

by javierhbr
star 0

Use when implementing authorization, restricting endpoints by permissions, setting up role-based access control (RBAC), checking user permissions, configuring RouteGuards, using GuardSetup presets, implementing ownership-based or team-based access, or adding wildcard/complex permission expressions in Noony handlers.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-middleware-development

by javierhbr
star 0

Use when creating custom middleware, adding cross-cutting concerns, intercepting requests or responses, implementing before/after/onError lifecycle hooks, passing data between middlewares via businessData, or accessing DI services inside middleware. PREREQUISITE — read `noony-middleware-ordering` first for where to place your middleware, then come here for how to build it.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-middleware-ordering

by javierhbr
star 0

THE canonical reference for middleware chain order — all other pipeline skills defer to this. Use when composing middleware pipelines, debugging execution order, fixing "response already sent" errors, understanding before/after/onError flow direction, sharing data between middlewares via context.businessData, or positioning any middleware in the chain.

navigation main article SKILL.md
schedule Updated 1 month ago
javierhbr

noony-complete-dual-entry

by javierhbr
star 0

The PRODUCTION PATTERN for Noony projects. Use when building a new project or graduating from `noony-create-fastify-server`. Complete dual-entry setup with Fastify (local dev) and Cloud Functions (production) working from the same handler code.

navigation main article SKILL.md
schedule Updated 1 month ago
Page 1 of 2

Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

Explore the agent skills ecosystem by occupation and creator

SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.

Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.

Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.

01 Map a field

Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.

02 Follow creators

Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.

03 Search with sources

Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.

Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)

In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.

Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.

The Structure of a Professional SKILL.md File

A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:

  • Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
  • Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
  • System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
  • Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
  • Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.

Optimizing Agent Workflows for Modern LLMs

Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.

Exploring by SOC Occupations and Creator Profiles

What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.

SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.