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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resume-assistant
by Y1fe1-Yang智能简历助手,通过五个AI代理提供全流程求职支持:(1)故事挖掘-发现经历亮点;(2)职位推荐-匹配合适岗位;(3)简历优化-针对JD定制内容;(4)模拟面试-实战演练与反馈;(5)能力提升-差距分析与计划。适用于简历创建、优化、面试准备、职业规划等求职相关任务。
academic-asset-generator
by Y1fe1-YangTransform raw academic ideas into professional four-asset packages (DOCX, PDF, PPTX, HTML poster) within a single session. Use when students or researchers need to create comprehensive academic deliverables from initial concepts, including research papers, presentations, conference posters, or thesis documents. Automates format layout and ensures consistency across all outputs through structured metadata as single source of truth.
film-creator
by Y1fe1-YangAI-powered film creation assistant that transforms a single sentence or image into a complete 30-second film. Automatically generates screenplay with scenes, dialogue, and camera directions, then produces cinematic video. Use when user wants to create a movie, film, or video story from text or image input. Trigger phrases: '创作电影', '生成电影', 'film creator', 'make a movie', 'create a film'.
happycapy-mcp-manager
by Y1fe1-YangConfigure multiple MCP servers (Composio, Memory, GitHub Copilot) globally in HappyCapy environment at /home/node/.claude.json. Use when users want to install MCP servers, access external apps (Gmail, Slack, GitHub, Notion), set up knowledge graph storage, configure OAuth integrations, or ask how to connect Claude to external services. Triggers include "install mcp", "configure composio", "setup memory mcp", "access gmail", "github copilot mcp", or any mention of connecting Claude to third-party apps.
happycapy-skill-creator
by Y1fe1-YangAutomate HappyCapy skill creation by finding and adapting existing skills from anthropics/skills repository. Handles environment constraints (Python 3.11, Node.js 24, no Docker). Use when user wants to create or adapt skills for specific tasks.
happycapy-customer-service
by Y1fe1-YangHappyCapy customer service agent. Use when user says "客服", "customer service", "support", "回复客户", "reply to customer", pastes a customer message, or asks to respond to a customer inquiry about HappyCapy.
ph-viral
by Y1fe1-YangGenerate Product Hunt forum posts that pass moderation and get 100+ comments. Based on analysis of 16 real high-engagement posts (168, 107, 73 comments). Creates natural, human-sounding content that sparks genuine discussion while avoiding promotional language and AI writing patterns. Use when creating Product Hunt forum posts, replying to comments, or planning content strategy. Generates three proven post types (Technical Depth, Casual Interactive, Community Support) with complete reply examples and timeline guidance. Credits: Built on HappyCapy platform - https://happycapy.com
morning-routine
by Y1fe1-YangAutomate morning workflows to kickstart the day. Execute multi-step routine including email summary, task extraction with AI suggestions, and personalized motivational image generation. Use when the user requests their morning routine, morning briefing, daily startup, or wants to automate checking emails, generating today's tasks, or creating morning motivation. Trigger phrases include "run my morning routine", "generate morning briefing", "start my day", "morning automation", or "daily kickoff".
capy-memory
by Y1fe1-YangMemory management for HappyCapy using Memory MCP. Enables Claude to remember user preferences, habits, and context across conversations. AUTOMATICALLY loads memory at session start. Triggers include automatic session initialization, explicit requests like "remember this"/"what do you know about me", implicit learning from stated preferences/corrections, and first-time memory setup.
desktop-pet
by Y1fe1-YangOne-command animated desktop pet generator. Converts any image into an interactive desktop pet with multiple animations (idle, walk, jump, happy, pet, sleep, etc.) and automatic preview. Use when user wants to create a desktop pet, animated character, screen companion, or mentions "desktop pet", "桌宠", "screen pet", "create pet from image", or uploads an image asking for a pet/companion. Generates web, extension, and desktop versions with drag interactions, click animations, and visual effects.
multi-platform-marketing
by Y1fe1-YangAutomate multi-platform content distribution from a single source. Generate tailored marketing content for Twitter, LinkedIn, Instagram, Pinterest, and Email from one input (article, brief, or concept). Creates a "Content Fingerprint" (SSOT) with core message, keywords, and visual concepts, then adapts tone, format, and style for each platform. Use when distributing content across social platforms, launching campaigns, or repurposing content for multiple channels. Trigger phrases include "post to multiple platforms", "adapt for social media", "multi-platform campaign", "cross-platform content", or "distribute across platforms".
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.