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...
bard-analyze
by hiivmindThis skill should be used when the user asks to "analyze this progression", "analyze these chords", "what key is this in", "what function does this chord have", "analyze my song", "what's the Roman numeral for", "explain this chord sequence", "break down this progression", "harmonic analysis of", or provides a chord sequence like "C Am F G" for analysis. Returns songwriter-friendly analysis with chord roles, voice leading, and Nine Harmonic Cells classification. Theory terms provided optionally in [Theory] tags.
bp-intent-create
by hiivmindThis skill should be used when the user asks to "create intent mapping", "add intent detection", "generate intent-mapping.yaml", "define keyword routing", "set up 3VL matching", "create routing rules", or needs to create intent-mapping.yaml for a gateway. Triggers on "intent create", "create intent", "intent mapping", "keyword routing", "3VL setup", "routing rules", "intent detection".
bp-intent-validate
by hiivmindThis skill should be used when the user asks to "validate intent mapping", "check intent rules", "verify routing", "lint intent-mapping.yaml", "check 3VL logic", "find routing gaps", or needs to verify an intent-mapping.yaml is correct. Triggers on "validate intent", "check intent", "verify routing", "lint intent", "3VL validation", "routing gaps".
bp-gateway
by hiivmindThis skill should be used when the user asks to "create gateway", "set up intent routing", "create intent mapping", "validate gateway", "add routing to plugin", "multi-skill routing", "update gateway". Triggers on "gateway", "intent routing", "intent mapping", "routing", "multi-skill", "3VL".
bard-corpus
by hiivmindThis skill should be used when the user asks to "find chromatic bass patterns", "show me bridge techniques", "deceptive cadence examples", "McCartney patterns", "how did Beatles do", "modal interchange examples", "search corpus for", "find patterns like", "show me examples of", "what patterns exist for", "browse the pattern database", or mentions specific techniques (chromatic bass, borrowed chords, modal interchange, deceptive moves). Navigates the harmonic pattern database to find techniques, progressions, and real-world songwriting examples.
bard-suggest
by hiivmindThis skill should be used when the user asks "where should I go next", "suggest a bridge", "what chord comes after", "how do I get to the chorus", "I'm stuck on this progression", "give me options for", "what would work here", "suggest chords for bridge/chorus/verse", "what should come next", "I need a pre-chorus", "help me write a bridge", or expresses being stuck or needing harmonic direction. Co-writing assistant that suggests next moves based on context using guitarist-friendly language. Theory provided optionally in [Theory] tags.
hiivmind-corpus-enhance
by hiivmindThis skill should be used when the user asks to "add more detail to corpus", "enhance corpus coverage", "expand section in index", "need more depth on [topic]", "corpus is too shallow", "add entries for [topic]", or wants deeper coverage of specific topics in an existing corpus. Triggers on "enhance [topic] section", "more detail on [feature]", "expand the index", "add depth to corpus", or "hiivmind-corpus enhance".
hiivmind-corpus-discover
by hiivmindThis skill should be used when the user asks "what corpora do I have installed?", "list my documentation corpora", "find installed corpus plugins", "what hiivmind-corpus plugins are available?", "check the docs", "search the documentation", "what do the docs say", "look up in corpus", or needs to discover installed documentation corpus skills. Also use when a generic documentation query doesn't specify which corpus to search.
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.