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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stalk-my-interviewer
by tinyfish-ioResearch an interviewer online before a meeting using parallel TinyFish agents and return a structured prep report. Use this skill when a user says "research my interviewer", "I have an interview with [name] at [company]", "stalk my interviewer", "find out about [person] before my interview", "who is my interviewer", "prepare for interview with [name]", "look up my interviewer", or any request to learn about a specific person before meeting them professionally.
clarify-resources
by yogsoth-aiUnderstand what resources the user has available for research — compute, timeline, collaboration, data access, experimental environment. Every item accepts 'TBD' as a valid answer.
ask-constraints
by yogsoth-aiUnderstand hard boundaries on the user's research — target venues, methodology preferences, areas to avoid, advisor/team requirements. Not limited to ML/AI — works for any research domain.
lockedin-render-interview
by daypunkDrafts an interview answer in English or Korean from the user's experience. STAR or PAR structure, two-turn writer/reviewer with a 5-dimension rubric. Activate when the user says <!-- ko-example -->"interview answer", "면접 답변", "STAR 답변", "tell me about a time…"<!-- /ko-example -->, or names a question and asks for an answer.
jotform
by ferosaiRetrieve form submissions from JotForm
typeform
by ferosaiRetrieve form responses or create forms in Typeform
insight-interview
by AbilityaiKB-grounded Socratic interview. Searches existing notes on a topic, then runs a one-question-at-a-time dialogue to surface, sharpen, and extract your own thinking. Ends by running extract-insights on the full conversation transcript.
deep-research-start
by allenhutchisonUse when the user wants to launch a Deep Research investigation — phrasings include "research X", "do a deep dive on Y", "investigate Z", "give me a comprehensive report on W", or any request for a multi-source synthesis that will take 10–30 minutes. Refines vague prompts into well-scoped research questions, suggests an appropriate output format (Executive Brief, Technical Deep Dive, Market Analysis, Comprehensive Research Report, or custom), helps the user pick file-search-store grounding when relevant, and then calls research_start with an outputPath so the report writes itself when the job completes.
skill-interview-builder
by irenerachel通过分步访谈引导用户理清需求，最终产出完整的Skill文件包（含SKILL.md、参考文档、示例文件等）， 并打包为可直接使用的压缩包。 当用户说"我想通过访谈新建Skill"、"用访谈方式做一个Skill"、"访谈建Skill"、 "通过访谈帮我生成Skill"、"访谈式创建Skill"、"我想访谈做一个XX的技能"时触发。 触发关键词必须包含"访谈"二字，不含"访谈"的Skill创建请求不由本Skill处理。 不用于已有完整SKILL.md只需小改的情况，也不用于一次性提示词请求。
ask
by buiducnhatStructured clarification and requirements gathering through focused dialogue or with dry code. Use when a task is ambiguous, underspecified, or requires user input before any action can be taken. Do not plan or implement anything—only ask questions to collect the information needed.
agent-ops-interview
by diegosouzapwConduct structured interviews with the user. Use when multiple decisions need user input: ask ONE question at a time, wait for response, record answer, then proceed to next question.
lark-workflow-form-builder
by liangdabiao智能表单收集器：创建多维表格和数据表单，AI 根据需求自动设计字段，配置表单问题，生成填写链接，分发到飞书群聊收集信息。当用户需要'创建表单'、'信息收集'、'制作问卷'、'收集反馈'、'报名表'、'数据收集'、'周报表单'时使用。
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.