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...
send-sticker
by openakitaSearch and send sticker images in chat. Use during casual conversations, greetings, encouragement, or celebrations to make interactions more lively and engaging.
conversation
by ECNU-ICALKGeneral SOP for common requests related to 天雷波动剑, 级技能, conversation.
bird
by CoWork-OSX/Twitter CLI for reading, searching, posting, and engagement via cookies.
vedic-career
by CNWU16吠陀占星(Vedic/Jyotish)职业分析引擎。当用户提供星盘PDF、截图或文本数据，并要求进行职业分析、事业解读、星盘事业方向分析时触发此技能。也在用户提到"印度占星"、"吠陀占星"、"Jyotish"、"星盘职业分析"、"D9分析"、"Navamsa事业"、"10宫分析"等关键词时触发。
hinduism-vedic-wisdom
by MicrockDeploy eternal navigation wisdom - Brahman, Atman, Satchitananda recognition active.
islam-sufism-wisdom
by MicrockIslamic and Sufi wisdom - Tawhid unity, surrender navigation, 99 Names positions, dhikr practice, fana/baqa dissolution
jainism-ahimsa-navigation
by MicrockDeploy non-violence as navigation itself - Ahimsa, Anekantavada, Syadvada active.
video-analytics-interpreter
by nicepkgInterpret YouTube Analytics, TikTok Analytics, and video performance data. Identifies trends, explains metrics, and provides actionable recommendations for growth. Use when analyzing video performance, understanding metrics, or optimizing channel strategy.
oracle-meihua-agent
by Bald0WangProduce short-term MeiHua YiShu analysis for concrete near-term events, including reframed question, tendency, key variables, do/donot guidance, and contingency actions. Use for today/this-week event decisions and tactical consultation.
media-curator-quickref
by jmaglyMedia-curator framework quick reference — capability domains and curated discovery phrases for discography analysis, source discovery, acquisition, quality filtering, metadata, and archive integrity
see-invisibility
by HmbownIn D&D, See Invisibility lets you see creatures and objects that have been made invisible. The real-world version is revealing deliberate obscurity: finding the hidden costs in a pricing page, uncovering the actual terms buried in a EULA, identifying the obfuscated tracking in a codebase, or surfacing the real behavior behind a misleading UI. Unlike Perception (which notices what is overlooked) or True Seeing (which pierces all illusion), See Invisibility specifically targets things that were hidden on purpose.
read-aloud
by CkokoskiText-to-speech read-aloud for manuscripts using free open-source Piper TTS with natural-sounding voices
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.