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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today
by jexchanGenerate today's prioritized plan based on daily notes, tasks, and current priorities. Use this skill whenever the user asks for today's plan, what to do today, daily priorities, planning the day, or wants a summary of today's focus areas. This skill integrates with the vault's task system and project next actions.
connect
by jexchanFind meaningful connections between two topics across the vault. Use this skill when the user asks to connect, link, or relate two concepts, find relationships between topics, discover how ideas intersect, or explore connections in their knowledge base. This skill searches Knowledge, Projects, and Daily Notes to identify direct links, bridge concepts, and synthesis opportunities.
trace
by jexchanTrack how a topic has evolved across the vault over time. Use this skill when the user asks to trace, track, or follow the evolution of a concept, see how an idea developed over time, find the history of a topic, or explore when and how a subject emerged in their knowledge base. This skill searches Daily Notes, Projects, and Knowledge files to build a timeline and knowledge network.
closeday
by jexchanEnd-of-day review and reflection. Use this skill when the user asks to close the day, end the day, do a daily review, reflect on today, summarize today's work, or wrap up the day. This skill reads today's Daily Note and projects' Next Actions to generate a structured review with completed items, insights, and tomorrow's priorities.
card-creator
by jexchanCreate knowledge cards from user input. Automatically detects card type if not specified, then fills in the appropriate template and saves to 04_Knowledge/00_Cards/. Use this whenever the user wants to create a knowledge card, note, or atomic piece of content for their knowledge base—even if they don't explicitly mention "cards" or "templates". Keywords: create card, new card, add note, save to cards, knowledge card, card from...
check-health
by jexchan定期审查 Obsidian 知识库健康状况，包括：矛盾观点检测、失效双向链接检查、孤立卡片识别。当用户说「检查知识库健康」、「检查链接」、「查孤立卡片」、「审查观点矛盾」或类似表达时触发此技能。
brain-storming
by jexchan围绕一个主题进行多维度发散性思维探索，帮助用户打破思维定式、发现新视角、产生创意联想。当用户说「头脑风暴」「brainstorm」「发散思维」「帮我想想」「探索一下」「多角度思考」「来点灵感」「思维发散」「围绕这个主题展开」「brainstorming」「给我一些想法」「发散一下」或表达需要围绕某个想法/主题进行深入探索时，务必使用此技能。即使只是简单说「想想这个」或「来个头脑风暴」，也应触发。
hello-world
by jexchanA simple skill that should be used to respond to a user when the enter the phrase "hello skills" or "hi skills".
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.