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...
bpmn
by guoliang1114-boopCreate business process diagrams using PlantUML syntax with BPMN, EIP, and Lean Mapping stencil icons. Best for workflow automation, approval chains, message-based integration patterns, and value stream mapping.
internal-audit-execution
by guoliang1114-boop基于IIA绩效标准执行内部审计项目，涵盖审计目标、范围、程序、抽样方法和工作底稿编制
internal-audit-annual-plan
by guoliang1114-boop基于IIA国际内部审计专业实务标准，制定风险导向的年度内部审计计划，涵盖审计宇宙、风险评分、资源分配
esg-assurance-preparation
by guoliang1114-boop准备ESG鉴证/保证工作，基于ISSB IFRS S1/S2、ESRS和GRI标准，评估ESG数据质量和可持续性报告内部控制
equity-incentive-tax
by guoliang1114-boop股权激励税务筹划：股票期权、限制性股票、RSU的税务处理与优化方案
tp-documentation-preparation
by guoliang1114-boop转让定价文档准备：主体文档、本地文档、国别报告，基于OECD BEPS第13项行动计划及中国国家税务总局公告2016年第42号
apa-arrangement
by guoliang1114-boop预约定价安排申请：可行性评估、申请流程、定价方法设计，基于OECD MAP指南及中国国家税务总局公告2016年第64号
beps-pillar-two-assessment
by guoliang1114-boopOECD支柱二GloBE规则评估：IIR、UTPR、QDMTT、15%最低税，基于OECD GloBE Model Rules及注释
cross-border-investment-tax
by guoliang1114-boop跨境投资税务架构：控股公司、融资安排、知识产权布局，基于税收协定网络、CFC规则及间接转让规定
audit-report-draft
by guoliang1114-boop起草审计报告，基于ISA 700/701/706框架，覆盖意见类型决策逻辑、关键审计事项、强调事项段等。
audit-risk-assessment
by guoliang1114-boop基于 ISA 315 (Revised 2019) 框架执行审计计划阶段的风险评估。当用户需要 (1) 制定审计计划 (2) 识别重大错报风险 (3) 评估内部控制 (4) 确定重要性水平 (5) 设计审计策略 时使用。输出结构化风险评估文档。
audit-substantive-procedures
by guoliang1114-boop设计和执行实质性审计程序，覆盖细节测试、实质性分析程序、函证程序和审计抽样，基于ISA 330框架。
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.