Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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co-brainstorm
by SnakeOBounce ideas off Codex. Use when you want fast alternative ideas, critiques, and perspectives on any topic. Triggers an interactive conversation with the Codex MCP server for brainstorming and exploration.
co-plan
by SnakeOGenerate a parallel plan via Codex. Use when you want an additional planning perspective to compare against your own plan. Runs in the background so you can continue working in parallel.
co-validate
by SnakeOGet a staff engineer review of your plan via Codex. Use when you want critical review feedback on a plan before finalizing it. Pass the path to the plan file as the argument.
debugexpress
by SnakeODebug Express.js and Node.js applications with systematic diagnostic techniques. This skill provides comprehensive guidance for troubleshooting middleware execution issues, routing problems, CORS errors, async error handling, memory leaks, and unhandled promise rejections. Covers DEBUG environment variable usage, Node Inspector with Chrome DevTools, VS Code debugging, Morgan request logging, and diagnostic middleware patterns. Includes four-phase debugging methodology and common error message reference.
debugfastapi
by SnakeODebug FastAPI applications systematically with this comprehensive troubleshooting skill. Covers async/await issues, Pydantic validation errors (422 responses), dependency injection failures, CORS configuration problems, database session management, and circular import resolution. Provides structured four-phase debugging methodology with FastAPI-specific tools including uvicorn logging, OpenAPI docs, and middleware debugging patterns.
debugflask
by SnakeODebug Flask applications systematically with this comprehensive troubleshooting skill. Covers routing errors (404/405), Jinja2 template issues, application context problems, SQLAlchemy session management, blueprint registration failures, and circular import resolution. Provides structured four-phase debugging methodology with Flask-specific tools including Werkzeug debugger, Flask-DebugToolbar, and Flask shell for interactive investigation.
debugflutter
by SnakeODebug Flutter applications systematically with this comprehensive troubleshooting skill. Covers RenderFlex overflow errors, setState() after dispose() issues, null check operator failures, platform channel problems, build context errors, and hot reload failures. Provides structured four-phase debugging methodology with Flutter DevTools, widget inspector, performance profiling, and platform-specific debugging for Android, iOS, and web targets.
debugkubernetes
by SnakeODebug Kubernetes clusters and workloads systematically with this comprehensive troubleshooting skill. Covers CrashLoopBackOff, ImagePullBackOff, OOMKilled, pending pods, service connectivity issues, PVC binding failures, and RBAC permission errors. Provides structured four-phase debugging methodology with kubectl commands, ephemeral debug containers, and essential one-liners for diagnosing pod, service, network, and storage problems across namespaces.
debuglaravel
by SnakeODebug Laravel applications systematically with this comprehensive troubleshooting skill. Covers class/namespace errors, database SQLSTATE issues, route problems (404/405), Blade template errors, middleware issues (CSRF/auth), queue job failures, and cache/session problems. Provides structured four-phase debugging methodology with Laravel Telescope, Debugbar, Artisan tinker, and logging best practices for development and production environments.
debugnestjs
by SnakeODebug NestJS issues systematically. Use when encountering dependency injection errors like "Nest can't resolve dependencies", module import issues, circular dependencies between services or modules, guard and interceptor problems, decorator configuration issues, microservice communication errors, WebSocket gateway failures, pipe validation errors, or any NestJS-specific runtime issues requiring diagnosis.
debugnextjs
by SnakeODebug Next.js issues systematically. Use when encountering SSR errors, hydration mismatches like "Text content did not match", routing issues with App Router or Pages Router, build failures, dynamic import problems, API route errors, middleware issues, caching and revalidation problems, or performance bottlenecks. Covers both Pages Router and App Router architectures.
debugnuxtjs
by SnakeODebug Nuxt.js issues systematically. Use when encountering SSR errors, Nitro server issues, hydration mismatches like "Hydration text/node mismatch", composable problems with useFetch or useAsyncData, plugin initialization failures, module conflicts, auto-import issues, or Vue-specific runtime errors in a Nuxt context.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
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.
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.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.