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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svg-animation-engineer
by liuyunlong2021-wqGenerates pure SVG code animations using flat design and CSS keyframes. Use when user asks for SVG code, Lottie alternatives, vector illustrations, or web-based motion graphics.
film-engineering-book
by liuyunlong2021-wqUse when converting raw script into a material engineering book before shot design. This stage translates audience-facing script into production-facing reusable material units such as dialogue coverage, action chain fragments, reveals, inserts, resets, and reactions for downstream shot design.
film-prop-asset
by liuyunlong2021-wqUse when analyzing script props for the current Banana/Veo short-drama pipeline. Output a dual-layer prop asset spec that is specific enough for detail-hungry downstream image models.
film-scene-asset
by liuyunlong2021-wqUse when turning script locations into a final empty-environment master-shot scene asset for the current Banana/Veo short-drama pipeline. This stage must inherit the selected type-analysis style and lighting, lock one reusable camera truth, and exhaustively enumerate everything visible in that master shot for downstream prompt assembly.
ltx-video-action
by liuyunlong2021-wqUse when converting the new shot-design output into LTX 2.3 image-to-video prompts for xiaolagumanju. This skill should read only compact downstream-useful fields such as first frame, last frame, shot prompt, dialogue, and duration, and should calculate final video duration from dialogue length at execution time.
banana-grid-shot-prompt
by liuyunlong2021-wqUse when converting shot-design results into Banana 3x3 storyboard grid JSON prompts for the current flashxiaolagu mainline. This skill is assembly-only and should write short natural-language cinematic beats, not rigid keyword chains.
narrato-docu
by liuyunlong2021-wq影视解说文案工坊。当你需要为视频/影片/纪录片撰写解说脚本时使用。支持粘贴SRT字幕或描述内容，输出带时间戳的结构化解说JSON。对照NarratoAI documentary/narration_generation.py。触发词：影视解说、视频解说、纪录片解说、写解说词、视频文案、解说脚本
narrato-short
by liuyunlong2021-wq短剧解说工坊。专为短剧/爽剧设计的解说脚本生成器，精通黄金开场、爽点放大、个性吐槽、悬念预埋等短剧解说核心技巧。对照NarratoAI short_drama_narration/script_generation.py。触发词：短剧解说、爽剧解说、短剧文案、剧情解说、短剧脚本、解说短剧
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.