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.
Querying local SQLite index...
tapd-idle
by wvlvikTAPD 项目管理平台集成。管理需求、缺陷、任务、工时、迭代、测试用例、Wiki等。**必须使用此技能当**: (1) 用户提到 TAPD、看板、敏捷、需求、缺陷、任务、迭代、工时、测试用例 (2) 需要查询/创建/更新项目实体或查看进度统计 (3) 用户说"看看我的任务"、"这周工时"、"有多少bug"、"需求进度"、"前端组任务"、"缺陷统计"等未明确提到TAPD但需要项目数据的场景。即使用户只说"看看任务"、"最近有什么bug"也应主动触发此技能。
jimeng-api-image-gen
by wvlvik(project - Skill) Generate AI images using Volcengine Jimeng API 4.0. Use when users request image generation from text prompts, image-to-image editing, or batch image creation. Triggers include "generate image", "create picture", "AI image", "Jimeng", "Seedream", or any request involving AI-powered image creation from descriptions.
alioss-upload
by wvlvikUpload files and directories to Alibaba Cloud OSS using internal oss-up binary (AK/SK baked in). Internal use only — do NOT release publicly. Use when users request "upload to OSS", "阿里云OSS上传", "upload images", "批量上传图片", or any file upload task involving Alibaba Cloud OSS.
jimeng-api-video-gen
by wvlvik(project - Skill) Generate AI videos using Volcengine Jimeng Video 3.0 Pro API. Use when users request video generation from text prompts or images, including text-to-video, image-to-video, or any AI-powered video creation. Triggers include "generate video", "create video", "AI video", "Jimeng video", "text to video", "image to video", or any request involving AI-powered video generation from descriptions.
performance-evaluation
by wvlvik(project - Skill) 团队绩效评分自动化处理。整合TAPD缺陷查询、Excel处理和智能体团队协作，实现月度绩效考核的自动化计算。使用场景：(1) 处理前端组绩效Excel表格 (2) 查询TAPD缺陷数据生成评分链接 (3) 计算绩效系数 (4) 更新汇总表。Triggers: "绩效", "考核", "评分", "绩效表", "coefficient"
skill-release
by wvlvik(project - Skill) Sync and publish skills from .agents/skills/ to skills/ directory with version bumping and git automation. Use when releasing or updating skills, triggered by "release skills", "publish skills", "sync skills", "deploy skills", or when skill development is complete.
tapd-idle
by wvlvikTAPD 项目管理平台集成。管理需求、缺陷、任务、工时、迭代、测试用例、Wiki等。**必须使用此技能当**: (1) 用户提到 TAPD、看板、敏捷、需求、缺陷、任务、迭代、工时、测试用例 (2) 需要查询/创建/更新项目实体或查看进度统计 (3) 用户说"看看我的任务"、"这周工时"、"有多少bug"、"需求进度"、"前端组任务"、"缺陷统计"等未明确提到TAPD但需要项目数据的场景。即使用户只说"看看任务"、"最近有什么bug"也应主动触发此技能。
alioss-upload
by wvlvikUpload files and directories to Alibaba Cloud OSS using Python SDK v2. Supports single file upload, multiple image upload with auto-renaming, batch directory upload, and large file resumable upload. Use when users request "upload to OSS", "阿里云OSS上传", "upload images", "批量上传图片", or any file upload task involving Alibaba Cloud OSS.
commit-review
by wvlvik微信小程序原生开发项目代码提交评审技能，检查架构约束、最佳实践和常见问题
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.