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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feishu-deck-h5-renderer
by FuQiangSubskill for the feishu-deck-h5 pipeline. Use after designer output exists: consume outline.json, local input assets, cloud asset records, style rules, and DeckJSON schema to produce deck.json and render the HTML deck. This subskill does not decide pitch strategy and does not publish.
feishu-deck-h5-simulator
by FuQiangRehearse how an approved or delivered H5 deck may land with customers, investors, leaders, buying committees, or internal stakeholders. Trigger on "模拟讲这套片子", "客户会怎么反应", "pitch rehearsal", or "帮我预演". Produces scenario forecasts, objections, talk-track notes, and revision queues, not real research.
feishu-deck-h5-validator
by FuQiangSubskill for checking and validating feishu-deck-h5 decks. Use for CHECK-ONLY review of existing HTML, post-render validation, compliance gates, text/language checks, visual audits, and delivery readiness. This subskill reports issues but does not redesign or publish.
feishu-deck-h5
by FuQiang总控 skill for Feishu / Lark-style HTML decks. Use for 飞书风格 PPT, Lark deck, 汇报材料, 客户提案, h5 deck, 16:9 网页演示, HTML deck generation/editing/validation, source parsing, Magic Page/Miaobi/html-box publishing, and feishu-slide-library importing. Routes work to subskills; generation is DeckJSON/render-deck first, normally raw-first, with validation before handoff or publish. For real `.pptx`, use a PowerPoint/keynote workflow instead.
feishu-deck-h5-designer
by FuQiangSubskill for the feishu-deck-h5 pipeline. Use after the controller routes a deck-generation request to design: turn user requirements plus local input/ knowledge and Feishu Base knowledge into a scenario, design_plan, and outline.json. This subskill does not render HTML or validate final visuals.
feishu-deck-h5-editor
by FuQiangOperations subskill for feishu-deck-h5. Use for existing deck edits, single-slide changes, reskinning foreign HTML, lift/swap from another deck, importing/converting existing PDF/PPT/HTML/docs, slide deletion/reorder, and round-trip recovery.
feishu-deck-h5-parser
by FuQiangSub-skill triggered when the user submits new source files or source URLs for feishu-deck-h5. Accepts PPT/PPTX/PDF/Keynote, Feishu/Lark docs, images, videos, audio, HTML decks, demos, and asset folders. Converts user content into current-task knowledge and reusable materials under input/runtime-library/. A .pptx is the single native-PowerPoint entry: it is converted by build_pptx into a structured `canvas` deck.json (code reconstruction of every element — text runs / embedded images / shapes — NO screenshots); un-reconstructable pages (live chart / SmartArt / OLE) become placeholders and are reported for the user to redo. The image / dual-background path is retired (不要图).
publisher
by FuQiangPublish a user-confirmed feishu-deck-h5 HTML deck to Feishu/Miaobi Magic Page. Use only after validator pass and explicit user confirmation. Do not validate, fix, render, rehearse, or ingest decks into feishu-slide-library.
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.