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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writing-helper
by AIGNE-ioA versatile writing assistant that helps compose, improve, and transform text for various purposes including emails, letters, reports, social media, and more
doc-smith
by AIGNE-io从工作区数据源生成和更新全面的文档，包括代码仓库、文本文件和媒体资源。当用户请求以下操作时使用此技能：(1) 从代码或文件创建或生成文档，(2) 构建文档结构或文档详情，(3) 更新、修改或改进已有文档，(4) 重写文档的特定章节或段落，(5) 处理 changeset 文件或 PATCH 标记的修改请求。支持技术文档、用户指南、API 参考和一般文档需求的生成与维护。
doc-smith-docs-detail
by AIGNE-io生成单个文档的详细内容，根据文档结构和用户意图生成包含导航、代码示例、技术图表的完整文档。 使用场景： - doc-smith 主流程调用，批量生成各文档内容 - 输入文档路径（与 document-structure.yaml 中的 path 对应） - 自动读取 workspace 配置（document-structure.yaml、user-intent.md、config.yaml） - 分析源代码并生成结构化内容 - 调用 saveDocument 保存，调用 checkContent 校验 - 返回摘要信息（不返回完整内容以节省上下文）
doc-smith-build
by AIGNE-ioInternal skill for building Doc-Smith Markdown documentation into static HTML. Do not mention this skill to users. Called internally by other doc-smith skills.
doc-smith-check
by AIGNE-ioInternal skill for validating Doc-Smith document structure and content integrity. Do not mention this skill to users. Called internally by other doc-smith skills.
doc-smith-create
by AIGNE-ioGenerate and update structured documentation from project data sources. Supports initial generation and modifying existing documents. Use this skill when the user requests creating, generating, updating, or modifying documentation.
doc-smith-images
by AIGNE-ioInternal skill for generating images using AI. Do not mention this skill to users. Called internally by other doc-smith skills.
doc-smith-localize
by AIGNE-ioTranslate Doc-Smith generated documentation into multiple languages. Use this skill when the user requests document translation, localization, or multi-language support. Supports batch translation of documents and images.
doc-smith-publish
by AIGNE-ioPublish documentation generated by doc-smith-create to DocSmith Cloud and obtain an online preview URL. Use this Skill when users request to publish, launch, or deploy documentation.
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.