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...
sea-navigation
by Colin4k1024Navigate the high seas using stars, compass, and nautical charts. Pirate-style navigation for treasure hunting voyages.
agent-yaml-authoring
by Colin4k1024Author and validate Superagent declarative YAML agent definitions (apiVersion: superagent/v1, kind: Agent). TRIGGER when: creating a new agent, editing configs/agents/*.yaml, reviewing agent spec fields, or when the user asks "how do I define an agent", "write me an agent yaml", "agent 配置怎么写". DO NOT TRIGGER when: working on workflow DAG nodes (use workflow-dag skill), pure Go backend logic, or infrastructure config.
mcp-integration
by Colin4k1024Patterns for integrating MCP (Model Context Protocol) servers and tools into Superagent agents. TRIGGER when: wiring an MCP server into an agent YAML, implementing a new MCP client/server, debugging "mcp tool not found" errors, or when the user asks "how do I add an MCP tool", "MCP 怎么接入", "connect filesystem MCP". DO NOT TRIGGER when: writing pure builtin tools (use builtin/<name> directly) or REST API integrations unrelated to MCP.
tool-middleware
by Colin4k1024Configure and extend the tool middleware chain (retry, timeout, rate-limit, cache) in Superagent. TRIGGER when: adding retry logic to a tool, configuring per-tool timeouts, implementing tool response caching, debugging "tool timeout" errors, or when the user asks "how do I add retry to a tool", "工具超时怎么设置", "tool middleware". DO NOT TRIGGER when: implementing the tool's core logic or writing MCP servers.
workflow-dag
by Colin4k1024Design and debug Superagent workflow DAG agents (type: workflow). TRIGGER when: designing a multi-step workflow, connecting DAG nodes, handling branch/condition logic, or when the user asks "how does workflow execution work", "DAG 怎么定义", "add a conditional branch to my workflow". DO NOT TRIGGER when: building single-model agents (use agent-yaml-authoring), orchestration with supervisor/sequential types, or simple tool-calling agents.
a2ui-streaming
by Colin4k1024Reference for the A2UI SSE streaming protocol used by Superagent agents to push typed events to the frontend. TRIGGER when: implementing a new event type, debugging streaming output, writing frontend SSE consumers, or when the user asks "how does streaming work", "what events does the agent emit", "A2UI 协议". DO NOT TRIGGER when: working on HTTP REST endpoints unrelated to streaming.
interrupt-resume
by Colin4k1024Implement and debug agent interrupt/resume (human-in-the-loop checkpointing) in Superagent. TRIGGER when: enabling interrupt on an agent, implementing resume API, debugging "checkpoint not found" errors, adding human approval steps, or when the user asks "interrupt/resume 怎么用", "how does checkpoint work", "pause agent for approval". DO NOT TRIGGER when: building non-interactive batch agents or simple one-shot queries.
model-routing
by Colin4k1024Guide for configuring and debugging the model routing layer in Superagent. TRIGGER when: adding a new model provider, configuring fallback chains, tuning cost/latency routing strategy, debugging "model not found" errors, or when the user asks "how does model routing work", "模型路由怎么配置", "add a new LLM provider". DO NOT TRIGGER when: writing agent YAML (use agent-yaml-authoring), implementing tool logic, or working on the UI.
multi-agent-orchestration
by Colin4k1024Design multi-agent systems using Superagent's supervisor, sequential, and parallel agent types. TRIGGER when: building orchestration agents, routing tasks between sub-agents, designing fan-out/fan-in pipelines, or when the user asks "multi-agent 怎么设计", "supervisor agent", "parallel agents", "agent orchestration". DO NOT TRIGGER when: working on single-model agents (use agent-yaml-authoring) or workflow DAG nodes (use workflow-dag skill).
observability
by Colin4k1024Set up and query Superagent's observability stack: OpenTelemetry traces, Prometheus metrics, and Grafana dashboards. TRIGGER when: debugging latency or errors via traces, adding custom metrics, configuring OTel collector, reading Grafana dashboards, or when the user asks "如何查看 trace", "metrics 怎么看", "observability 怎么接入", "add a span to my code". DO NOT TRIGGER when: working on agent logic, model routing, or frontend UI.
lab-experiment
by Colin4k1024Design and execute scientific laboratory experiments with rigorous methodology, controls, and statistical analysis.
treasure-hunt
by Colin4k1024Plan and execute pirate treasure hunts — map reading, crew coordination, X-marks-the-spot strategies, and sea navigation tips.
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.