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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elixir-docs
by wunkiWrites production-quality @moduledoc, @doc, and @typedoc annotations for Elixir modules. Scales doc shape to module complexity: one-line docs for simple wrappers, multi-section guides with examples for core concepts. Applies ExDoc conventions including cross-references, admonitions, doctests, tables, options lists, and return contracts. Use when asked to "document modules", "add moduledocs", "write elixir docs", "doc standard", "add @doc", "improve documentation", or when Elixir modules have thin, missing, or inconsistent documentation. Don't use for README files, user-facing guides, ExDoc configuration, hex.pm publishing, mix docs generation, non-Elixir projects, or LiveBook notebooks.
stop-slop
by wunkiDetects and removes AI writing patterns from prose to make text sound human. Use when the user asks to clean up, edit, review, or rewrite text that sounds like AI, contains AI tells, or reads as generic filler. Use when drafting new prose with explicit instructions to avoid AI patterns or sounding robotic. Don't use for grammar fixes, spell-checking, general tone rewrites, style changes unrelated to AI patterns, code review, SEO optimization, or translation. Don't use if the user wants the writing to sound more formal or professional without mentioning AI patterns specifically.
guide
by wunkiCoaches the user through completing a task themselves instead of doing it for them. The core signal is that the user wants to remain the actor and build understanding, not receive a finished result. Use when the user explicitly signals they want to be in the driver's seat: phrases like "guide me", "teach me", "help me learn", "I want to understand how to do this", "don't do it for me", or "let me figure it out". Don't use when the user says "show me how" or "walk me through" without pairing it with a desire to do the work themselves (these usually mean "just show me"). Don't use for requests that want a task completed directly, a bug fixed, a document produced, a one-shot explanation without back-and-forth, or general Q&A. Don't use when "help me understand X" means "explain X" without requesting interactive back-and-forth. Don't use when "help me work through" refers to debugging or problem-solving assistance, not skill-building.
interview
by wunkiConducts exhaustive requirements-gathering interviews for software features or systems. Reads existing context, then asks structured numbered questions covering purpose, technical design, UI/UX, edge cases, security, and rollout. Use when the user says "interview me", "deep dive on requirements", "spec this out", "fill out the spec", "help me think through this feature", or when a feature needs thorough requirements elicited before a spec or plan can be written. Don't use for quick clarifying questions during an in-progress task (use ask-questions-if-underspecified instead), for writing or updating a SPEC.md document (use create-spec instead), for generating a task plan (use create-plan instead), or for general architectural discussions that don't need structured requirements output.
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.