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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heartbeats
by leprachuanUse when you want to schedule work to happen later, remind yourself to follow up on something, or queue a task for the next hour. Write deferred instructions into HEARTBEAT.md — an hourly scheduled task will pick them up, execute them as background tasks, and clear the file. Trigger phrases: "remind me later", "check this in an hour", "do this after", "follow up on", "schedule this for later", "queue this up", "deferred task", "next time you wake up". IMPORTANT: You can and should use this proactively on your own initiative — not just when the user asks. Use it to follow up on background tasks you spawned, verify something completed correctly, check on a deployment, circle back on an open question, or pursue any goal that benefits from a future check-in. Think of it as leaving yourself a note for your next wakeup.
heartbeats
by leprachuanUse when you want to schedule work to happen later, remind yourself to follow up on something, or queue a task for the next hour. Write deferred instructions into HEARTBEAT.md — an hourly scheduled task will pick them up, execute them as background tasks, and clear the file. Trigger phrases: "remind me later", "check this in an hour", "do this after", "follow up on", "schedule this for later", "queue this up", "deferred task", "next time you wake up". IMPORTANT: You can and should use this proactively on your own initiative — not just when the user asks. Use it to follow up on background tasks you spawned, verify something completed correctly, check on a deployment, circle back on an open question, or pursue any goal that benefits from a future check-in. Think of it as leaving yourself a note for your next wakeup.
live-canvas
by leprachuanLive visual canvas for agents. Use when running multi-step tasks (deploys, installs, batch jobs), gathering config from the user (forms), showing plan approval (flowchart + buttons), or displaying live dashboards (metrics, charts, tables). The canvas opens a browser panel that updates in real time as the agent pushes components over WebSocket. Users can click buttons or submit forms and the agent receives the action. Server auto-starts on first use.
task-scheduler
by leprachuanUse when you need to schedule tasks to run at specific times or intervals - supports natural language timing like "every day at 9am" and manages job creation, listing, pausing, resuming, and deletion
wee-canvas-todos
by leprachuanInteractive TODO board for Wee Canvas. Displays TODOs from both GitHub Issues (leprachuan/fosterbot-home) and flat files in two views: list and kanban. Features filtering, drag-and-drop status changes, quick-add, and auto-refresh every 30 seconds. Use when Foster asks to "show TODOs", "open TODO board", "view my tasks", or "TODO kanban".
product-researcher
by leprachuanResearch products and display results on a live canvas with product images, prices, star ratings from multiple sources (Amazon, Google, Reddit, review sites like Wirecutter/RTINGS/PCMag), pros/cons, and working buy links. Use when the user wants to compare products, research before buying, find the best options in a category, look up reviews, or says things like "research X", "compare these products", "find the best Y", "show me reviews for", "help me pick a backpack/laptop/camera/headphones/etc", or attaches a screenshot of products from a shopping site. Renders everything on the live canvas (requires live-canvas skill).
skill-security-scanner
by leprachuanScan AI agent skills for security vulnerabilities - prompt injection, credential exposure, malicious code patterns, data exfiltration risks, and dangerous function calls. Outputs severity-rated findings with remediation guidance.
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.