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...
prd
by iannuttallGenerate a Product Requirements Document (PRD) as JSON for Ralph. Triggers on: create a prd, write prd for, plan this feature, requirements for, spec out.
commit
by iannuttallWrite conventional commit messages with type, scope, and subject when the user wants to commit changes or save work.
librarian
by iannuttallUse Librarian to fetch and search up-to-date developer documentation from GitHub and websites so answers are grounded in real docs.
get-url
by iannuttallFetch any URL as markdown with `npx playbooks get`. Use whenever you need to retrieve web content from a URL to get clean markdown for analysis or quoting.
find-skills
by iannuttallFind and install agent skills with `npx playbooks find skill` and `npx playbooks add skill`. Use whenever a skill needs to be discovered or installed.
chrome-webstore-release-blueprint
by iannuttallGuide a user end-to-end through setting up Chrome Web Store API release automation in any repository. Use when asked to walk someone through OAuth/CWS credential setup, refresh token creation, local/CI secret setup, version-based publish automation, and submission status checks.
figma
by iannuttallRead-only Figma REST API workflows for file inspection, audits, token extraction, typography analysis, component listing, asset export, comment retrieval, text extraction, text search, node style inspection, node diffing, and combined node inspection. Use when a user provides a Figma file key or URL and needs implementation-facing design data without opening Figma manually.
telegram
by iannuttalltelegram-specific inline directives you embed in your response text to react, reply-to, or control streaming. NOT a CLI tool - these are magic strings that get parsed out of your telegram response.
schedule
by iannuttalluse when you need to set a reminder, schedule a task for later, create recurring jobs, check back on something in the future, or wake yourself up at a specific time. triggers include "remind me", "in an hour", "tomorrow", "every day", "later", "follow up", "check back", "schedule", "timer", "alarm", "recurring", "cron", and similar.
memory
by iannuttalluse when you need to remember something, recall past decisions, look up what the user told you before, store preferences, save important facts, or search your memory for context. triggers include "learn", "remember", "forget", "recall", "what did i say", "did i mention", "store", "save", "memory", "notes", "log", and similar.
events
by iannuttalluse when writing background scripts that need to notify you on completion or failure, or when you need to wake yourself up immediately from an external trigger. triggers include "background task", "script finished", "notify on complete", "wake me when done", "event hook", "callback", "on error", "on success", and similar.
dnd
by iannuttalluse when user wants to enable do not disturb, go afk, not be messaged for a while, or is going into a meeting/appointment. triggers include "afk", "dnd", "do not disturb", "don't message me", "going into a meeting", "be back in", "brb".
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.