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.
Querying local SQLite index...
pi-ralph-wiggum
by tmustierLong-running iterative development loops with pacing control and verifiable progress. Use when tasks require multiple iterations, many discrete steps, or periodic reflection with clear checkpoints; avoid for simple one-shot tasks or quick fixes.
skill-creator
by tmustierCreate or update Agent Skills (SKILL.md plus optional scripts, references, or assets). Use when someone asks to design a new Agent Skill, refine an existing one, or structure skills for Pi discovery, packaging, or other Agent Skills-compatible clients.
extending-pi
by tmustierGuide for changing or extending Pi's behaviour. Use when someone wants to modify how Pi behaves, add capabilities, decide which Pi extension point or artifact to use, build or package an extension, create an Agent Skill, add prompt templates/themes/context/model providers, configure Pi resources, or asks whether a Pi internal patch is needed.
mcp-gateway
by tmustierDiscover and call tools from configured MCP servers. Use when external capabilities are needed beyond built-in workbook tools.
web-search
by tmustierSearch the public web for up-to-date facts. Works out of the box with Jina (default, no API key needed); optionally Serper, Tavily, or Brave Search. Use when workbook context is insufficient and fresh external references are needed.
tmux-bridge
by tmustierLocal terminal access via the tmux bridge. Use when the user asks about running shell commands, setting up the tmux bridge, or troubleshooting terminal connectivity.
python-bridge
by tmustierNative Python execution via the local Python bridge. Use when the user asks about running Python locally, setting up the Python bridge, LibreOffice conversion, or troubleshooting Python connectivity.
extending-pi
by tmustierPlan and build Pi for Excel extensions safely: choose skill vs extension plugin vs connection, create flat SKILL.md skills, and handle API keys without asking users to paste secrets in chat.
agent-teams
by tmustierCoordinate multi-agent teamwork with shared task lists, mailbox messaging, and long-lived teammates. Use when the user asks to spawn workers, delegate tasks, work in parallel with agents, or manage a team of workers.
clean-slides
by tmustierInspect, edit, and generate PowerPoint slides. Progressive drill-down inspection, text editing with formatting preservation, table generation from YAML, and PNG rendering for verification.
heypocket-reader
by tmustierRead transcripts and summaries from Pocket AI (heypocket.com) recording devices. Use when users want to retrieve, search, or analyze their Pocket recordings, transcripts, summaries, or action items.
pi-symphony
by tmustierOperate pi-symphony — unattended issue orchestration that polls Linear, creates isolated workspaces, and launches Pi workers to implement issues autonomously. Use when the user asks about symphony, setting up a WORKFLOW.md, launching or monitoring symphony runs, preparing Linear issues for orchestration, checking symphony status, or anything related to autonomous/unattended issue execution. Also triggers on "orchestration", "unattended", "autonomous issues", "symphony dashboard", "symphony preflight", or "WORKFLOW.md".
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.