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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pdf-translator
by ForceInjectionExtract text from PDF files, translate it to a target language, and save the result as a Markdown file. Use this skill when the user wants to translate a PDF document or asks to "convert PDF to Chinese".
code-reader
by ForceInjectionUse when you want to deeply understand an unfamiliar codebase and generate reusable cognitive skills from it, by providing a local path or GitHub URL
agent-skill-reviewer
by ForceInjectionReview Agent Skill directories and SKILL.md files against best practices. Use this skill when the user wants to review, validate, or check an Agent Skill implementation.
dir-organizer
by ForceInjection整理和优化项目目录结构。当用户请求整理目录、分类文件、清理无用文件或重构文件夹结构时调用此技能。
doc-reviewer
by ForceInjection审查技术文档。支持四种独立评审类型：大纲评审（检查目录与结构逻辑）、内容评审（检查文字准确性与代码质量）、资产评审（校验链接与引用合规）、格式评审（校对纯视觉排版与标点）。当用户请求审查或修正 Markdown 文档时使用。
drawio-designer
by ForceInjectionCreates, edits, and manages draw.io XML diagrams, converts them to PNG, and integrates AWS, K8s, and General IT icons. Invoke when the user wants to create, modify, or format draw.io architecture diagrams or flowcharts.
editorial-card-designer
by ForceInjectionDesign high-density editorial HTML info cards in a modern magazine and Swiss-international style, then render them as ratio-specific PNG screenshots. Use when the user provides text or core information and wants: (1) a complete responsive HTML info card, (2) the design to follow the stored editorial prompt, (3) output in fixed visual ratios such as 3:4, 4:3, 1:1, 16:9, 9:16, 2.35:1, 3:1, or 5:2, or (4) both HTML and a rendered PNG cover/card from the same content.
md-link-checker
by ForceInjection检查 Markdown 文件中的本地和外部链接有效性。当用户需要验证、检查 Markdown 文档或项目的链接可访问性时调用此技能。
md-summarizer
by ForceInjection分析和总结指定的本地 Markdown 文件，并输出结构化的中文总结。当用户请求总结、分析或提取本地 Markdown 文档信息时调用此技能。
md-translator
by ForceInjection将指定的本地 Markdown 文件翻译成指定语言（默认中文），并在文件名中添加语言标识后缀。当用户请求翻译本地 Markdown 文档时调用此技能。
ontology
by ForceInjectionTyped knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.
openspec-assistant
by ForceInjection执行 OpenSpec 规范驱动开发 (SDD) 。涵盖意图对齐、规范生成、代码实现与自动化验证。支持架构师 (写Spec/评审) 、开发 (写代码) 和 QA (写测试) 角色协同及 /opsx 指令体系。
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.