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...
learning-plan
by troystaylorCreates structured learning plans from Microsoft Learn documentation. Use when the user asks to "create a learning plan", "help me learn", "what should I study for", "build a study guide", "prepare for certification", "get up to speed on", "onboard me to", or wants to learn a Microsoft technology from scratch or deepen their expertise.
research-docs
by troystaylorSearches and retrieves Microsoft documentation. Use when the user asks to "find docs about", "how do I", "what is", "look up", "search Microsoft Learn", "find the article about", "get the latest docs on", or asks any question about Microsoft or Azure products, services, or APIs.
compare-services
by troystaylorCompares Microsoft and Azure services, features, or approaches side by side. Use when the user asks to "compare", "difference between", "which should I use", "pros and cons of", "Azure Functions vs Container Apps", "should I use X or Y", or any question that involves choosing between Microsoft technologies.
report-and-summarize
by troystaylorGenerates reports, summaries, and analysis from {{Service Name}} data. Use when the user asks to "summarize", "report on", "give me an overview", "analyze", "break down", "how are we doing on", "what's the trend", or "weekly/monthly update" for {{Service Name}} data.
slack-people-lookup
by troystaylorFinds people in Slack by name, handle, email, or role description, and returns their profile and availability. Use when the user asks "who is @jane", "who runs #incidents", "find the on-call for payments", "who's the Slack admin", or "what's @bob's email and timezone".
calendar-guardrails
by troystaylorGuidelines for calendar and meeting operations. Apply when the agent creates, modifies, or queries calendar events. Use alongside discover_graph results to shape behavior.
cancel-upload
by troystaylorCancel an in-flight upload session. Use when the user asks "cancel the upload", "stop the file transfer", "abort the upload", "kill the upload session", or "I don't need that upload anymore".
copy-across-sites
by troystaylorCopy files or folders between SharePoint sites or document libraries. Use when the user asks "copy this file to another site", "duplicate this document to the other library", "copy from Site A to Site B", "replicate this folder to the team site", "clone this file to another SharePoint library", or "move this to another site" (cross-site move is copy + delete).
planner-tasks-guardrails
by troystaylorGuidelines for Planner and To Do task operations. Apply when the agent creates, assigns, updates, or queries tasks, plans, and buckets.
troy-s-email-style
by troystaylorTroy Taylor's email preferences. Apply when composing or formatting email for troy.taylor@zava.com.
troy-s-meeting-prep
by troystaylorTroy Taylor's meeting preparation preferences. Apply when Troy asks to prepare for a meeting, review agenda, or get ready for a call.
troy-s-weekly-summary
by troystaylorTroy Taylor's weekly summary email preferences. Apply when Troy asks for a weekly summary, week recap, or status report to send to his manager.
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.