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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apple-health-analysis
by labrinyangClinical-grade deep analysis of Apple Health export data. Parses the XML from iPhone's Health app export and produces a comprehensive clinical-quality health assessment report using 20+ peer-reviewed statistical methods including Granger Causality, Transfer Entropy, Convergent Cross Mapping, Sample Entropy, DFA, Cosinor analysis, Kovatchev glucose risk indices, Bayesian change-point detection, and biological age estimation. Use this skill whenever the user mentions Apple Health data, health export, health XML, CGM data, wants to analyze their fitness/sleep/heart rate/glucose/weight trends, or has an `apple_health_export` directory in their project. Also trigger when the user asks about health data analysis, wearable data analysis, or wants insights from their Apple Watch or iPhone health data. Even if they just say "analyze my health" or "look at my health data" and there's an Apple Health export nearby, use this skill. Trigger on Chinese equivalents too: "分析健康数据", "健康报告", "Apple Watch 数据".
cowork-with-onboarding
by labrinyangGuides MCP setup for Atlassian and Figma integration. Use when the user needs to set up, configure, or troubleshoot MCP server connections for Jira, Confluence, or Figma.
cowork-with-figma
by labrinyangUse when the user works with or mentions Figma designs, prototypes, mockups, UI specs, design tokens, or design-to-code workflows
cowork-with-jira
by labrinyangUse when the user works with or mentions Jira issues, tickets, stories, bugs, tasks, sprints, epics, or any agile workflow
cowork-with-wiki
by labrinyangUse when the user works with or mentions Confluence wiki, product docs, documentation pages, or knowledge base content
smart-execute
by labrinyangUse AFTER a plan with task list exists and the user has approved execution. Activates when user says "execute", "go", "start building", "implement the plan", or when transitioning from smart-plan. Also use when user says /bpe:execute.
linear-cowork
by labrinyangUse when working on any project that uses Linear for issue tracking, or when the user mentions Linear issues, tickets, creating issues, updating issue status, linking commits to issues, or any Linear MCP interaction. Always activate this skill for issue creation, status updates, commit workflows, and project organization in Linear.
protocol-info-batch-operator
by labrinyangFocused sub-skill selected by protocol-info-router for long protocol-info batches, manual queues, background crawls, ScheduleWakeup/task-notification issues, stuck crawls, killed batches, and throughput diagnosis. Use after the router chooses the batch-operations path. Do not use for one-off crawls or record-field edits.
protocol-info-crawler
by labrinyangFocused sub-skill selected by protocol-info-router for creating or recrawling DeFi yield/earn protocol metadata into a schema-validated EarnProtocolInfo record. Use after the router chooses the new-crawl path. Do not use for existing-record maintenance, queue orchestration, token prices, on-chain data, or generic web research.
protocol-info-maintainer
by labrinyangFocused sub-skill selected by protocol-info-router for maintaining an existing out/<slug> protocol-info record. Covers get/set/analyze, refresh, i18n, pdf-text, history/diff, and restore. Use after the router chooses the existing-record path. Do not use for new crawls, queue orchestration, token prices, or on-chain queries.
protocol-info-router
by labrinyangMain router skill for the protocol-info plugin. Use for any request about DeFi EarnProtocolInfo records, protocol-info crawls, existing out/<slug> maintenance, i18n, audit PDF text, history/diff/restore, long batch queues, stuck/background crawls, or plugin workflow selection. It chooses the focused protocol-info sub-skill before dispatching. Do not use for unrelated DeFi questions, token prices, on-chain analytics, or generic web research.
synai-relay
by labrinyangGuide for AI agents to earn and spend USDC on SYNAI Relay via MCP tools. Use this skill whenever you have synai_* MCP tools available and need to: browse/claim/complete tasks for USDC payment, post tasks for other agents, check earnings or submissions, manage jobs (fund/cancel/refund/dispute), or interact with the SYNAI Relay platform in any way. Also activate when a user mentions SYNAI, agent tasks, earning USDC, x402 payments, or agent-to-agent task trading.
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.