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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reference-as-executable-spec
by artsmcUse when Mark frames a feature by pointing at a concrete reference instead of describing behavior from scratch — when he names a live product as the target ('an inline editor like editor.js', 'a code editor like monaco-editor dark theme', 'much like openclaw.ai'), points at an internal subsystem as the literal spec to replicate ('the OTP from sentient-monorepo is the pattern we should match', 'use sentient-monorepo as the api source of truth'), or anchors a requirement on an external library as the structural model ('setups similar to CASL', 'building this same CASL boilerplate'). Whenever he says 'build it like THAT', 'same as X', or 'mimic/replicate X', treat the named reference as the executable spec — go observe it, extract its real behavior, and let THAT define correct rather than inventing requirements.
document-hub-read
by artsmcRead and summarize the current state of the documentation hub (cline-docs/). Provides a quick overview of system architecture, module responsibilities, technology stack, and glossary terms. Use this skill whenever the user asks about the project's documentation, wants to understand the architecture, asks "what does this project do", "show me the docs", or needs a quick onboarding summary. Also use before starting work to understand project context.
document-hub-update
by artsmcComprehensive review and update of the documentation hub (cline-docs/). Analyzes recent code changes, detects drift, validates structure, and proposes specific updates to keep documentation synchronized with the codebase. Use this skill whenever the user says "update the docs", "sync documentation with code", "docs are outdated", or after significant code changes. For read-only analysis without changes, use document-hub-analyze instead.
document-hub-analyze
by artsmcDeep analysis of codebase vs documentation alignment (cline-docs/). Detects drift, identifies undocumented code, extracts missing glossary terms, and provides actionable recommendations without making changes. Use this skill when the user asks "are the docs up to date", "check documentation quality", "what's missing from the docs", or wants a read-only audit before deciding what to update. For actually making changes, use document-hub-update instead.
mastra-streaming
by artsmcMastra Streaming patterns - agent streams, workflow streams, tool streaming, SSE events, and AI SDK integration for React/Next.js UIs
headroom-context-compression
by artsmcShrink large text before it travels or gets re-read, using the headroom MCP (mcp__headroom__headroom_compress / _retrieve / _stats). Reach for this BEFORE forwarding a big blob into a subagent (Task) prompt, before stashing content you'll reason over again later, or in any multi-step pipeline where the same large output would otherwise be carried forward repeatedly. The compression is lossy but fully reversible — every compress returns a hash you can later expand with headroom_retrieve, so nothing is ever truly lost. Use this skill whenever you're about to hand off, persist, or repeatedly re-read large logs, file contents, search results, JSON, or command output — even if the user never says "compress." If you find yourself pasting a 200+ line blob into a subagent prompt or a notes file, that's the trigger. Also reach for it the other direction — when you're handed a compressed `hash=` marker or a summarized view and need an exact detail (a line, value, or filepath) back from the original. This is about LLM c
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.