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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tapcanvas-continuity
by anymouschina处理章节分镜续写、storyboardChunks、tailFrameUrl 与连续性审查，确保续写边界与尾帧承接可追溯。
tapcanvas-storyboard-expert
by anymouschina统一的 TapCanvas 章节分镜专家。用于“漫剧创作/章节剧本/分镜提示词/Seedance 片段脚本/章节出镜头”任务，默认输出 storyboard-director/v1.1 JSON，同时内置 Seedance 时间轴片段脚本、资产规划、对白/OS/VO/闪回格式与连续性收口方法。
aesthetic-audit
by anymouschina商业级视觉资产与网页(详情页/落地页)的审美与UX视觉审查：输出P0/P1/P2问题清单、可落地的样式tokens与改稿建议；可结合截图、URL与源码进行定位与修改。
generate-media
by anymouschina短剧媒体生成技能。基于已生成作品目录与视觉风格生成角色卡、场景/道具参考图与分镜图，维护 ref_index 与 media_index。涉及 TapCanvas `/public/draw`、`/public/tasks/result` 的实际接口请求时，必须统一通过 `tapcanvas-api` skill 执行，而不是在本 skill 内维护另一套 API 配置。
tapcanvas-prompt-specialists
by anymouschina定义 image_prompt_specialist、video_prompt_specialist、pacing_reviewer 的职责边界、触发条件与最小输出契约；不强制固定调用顺序。
tapcanvas-replicate
by anymouschina多资产复刻能力：基于 assetInputs（N 张图）执行角色或主体替换，保持版式与镜头连续性。
tapcanvas-video-prompting
by anymouschina处理关键帧转视频、短视频规划、节奏控制与拆段判断；输出最小可执行的视频提示词结果，不把 SOP 固化进后端 prompt。
agent-builder
by anymouschina设计并构建各类 AI 智能体/助手。适用于用户： (1) 询问“创建 agent / 助手 / 智能体系统” (2) 想理解 agent 架构、agentic 模式或自治式 AI (3) 需要能力设计、子代理、规划或 skills 机制建议 (4) 询问 Claude Code、Cursor 等智能体内部实现 (5) 想为业务/研究/创作/运营等场景构建 agent 关键词：agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
agents-team-book-metadata
by anymouschina启用 agents-team 协作，双代理完成小说逐章元数据抽取与完整性审校（parser + checker），并写入可续跑的记忆索引。
agents-team
by anymouschinaEnable general multi-agent team mode via spawn_agent/wait tools. Supports orchestrator, worker, reviewer, research, writer, and editor roles.
code-review
by anymouschina进行全面代码审查，覆盖安全、正确性、性能与可维护性；适用于用户要求 review、排查潜在 bug 或审计代码库。
cognitive-memory
by anymouschinaAgents-CLI 认知记忆系统。用于管理长期记忆（core/episodic/semantic/procedural/vault）、可检索回忆、归档遗忘、以及多代理写入治理。
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.