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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contract-review-pro
by CSlawyer1985专业合同审核 Skill，基于合同审核工作区成熟经验，提供7步工作流、终稿三件套、风险六维度评价
china-lawyer-analyst
by CSlawyer1985通过中国法律视角分析事件，运用成文法解释、指导案例参照、请求权基础分析等方法， 理解权利义务、评估责任风险、识别法律依据并推荐合规策略。 **混合架构 v3.3**：三阶段自动化工作流程（初步分析→法律校验→反思修正）+ 静态核心 + 动态司法解释索引 + 智能检索增强 + 45类案件精确识别 + 六段式要件清单。
mineru-converter
by CSlawyer1985PDF转Markdown工具，基于MinerU和方案A++策略，完全封装的CLI工具+Skill，自动清理临时文件，支持单文件和批量处理
wechat-article-exporter
by CSlawyer1985微信公众号文章批量导出工具 - 支持Docker本地部署，实现三大场景的自动化工作流：法律研究和知识管理（每周批量下载法律公众号文章，导出Markdown到Obsidian）、竞品分析（下载竞品律所全部历史文章，导出Excel分析内容策略）、AI训练数据准备（批量下载特定领域文章，导出JSON用于模型微调）。支持Markdown、HTML、Excel、JSON四种格式导出
financial-statement-analyst
by CSlawyer1985专业财报解读分析师，融合各家所长，基于宏观-中观-微观三层方法论体系， 提供专业级财报解读、舞弊识别和价值评估服务。 **混合架构**：核心模块+行业模块+方法论模块+智能三级路由， 支持200+财务指标、70+预警信号、行业对标数据库、30+指标详解。 **v1.6.0**：核心指标详解体系（30+个指标详解文件）、 完整7大行业模块（制造、房地产、金融、消费、科技、医疗、公用事业）、 战略分析模块、财务思维模块、八维战略分析框架、产业链分析模块、 价值投资模块、穿透式分析模块、快速排雷检查清单、选股检查清单、 资产负债表舞弊痕迹识别、财务分析核心技术、快速诊断技巧、 表外资源分析（品牌、文化、商业模式、人力资源）、 第一性原理估值分析（PE/PB陷阱深度剖析、DCF推导）、 会计方法可控性分析（原则/方法/证据的可选择性）、 新收入准则深度解读（五步法模型、控制权转移）、 存货计价方式操纵（FIFO vs 加权平均、通胀/通缩影响）、 预付款项风险分析（预付工程款、预付专利款、资金循环识别）、 完工百分比法风险预警（预算上调、毛利率波动、工程量vs成本进度）、 非GAAP指标操纵（调整后净利润、EBITDA陷阱、自由现金流定义操纵）、 合并报表vs母公司报表分析（战略差异识别、依赖度分析、资金往来风险）、 多角度多层次数据分类（7大维度分类：业务/产品/区域/客户/模式/收款/季度）。 **经典案例库**（17个详细案例）：安然、世通、微策略、冠群电脑、美国在线、 绿山咖啡、新泰辉煌、威帝斯半导体、英特尔、 康美药业、康得新、乐视网、汉能薄膜、保千里、 辉山乳业、万福生科、神雾环保。 快速排雷(5分钟)、深度分析(30分钟)、舞弊识别、价值评估四大标准流程。
batch-image-pdf
by CSlawyer1985Use when the user wants to turn source content into a coherent batch of AI-generated visual pages, posters, route books/路书, illustrated roadbooks, visual decks/PPT-style pages, hand-drawn-style PPT, image-model-generated PPT, social carousel posters, concept poster series, or any multi-page text-image visual output. Trigger on requests like 批量生成图片/海报, 生成路书/录书, 生成图文PPT, 用生图模型生成PPT, 手绘版PPT, 批量用生图模型生成视觉页, 把文档做成一组海报/PDF/PPT. This skill covers interactive clarification, recommending page count, aspect ratio and style, semantic decomposition, information-density planning, integrated text-image prompt design, generating each finished page with ChatGPT Images 2.0/image_gen, saving final assets, writing prompts.md, and assembling images into an ordered PDF by default.
data-observatory-dashboard
by CSlawyer1985Use when transforming arbitrary structured or semi-structured data into a high-density, source-aware, interactive data visualization dashboard. Fits CSV, Excel, JSON, webpage tables, public datasets, research/policy/legal/financial/industry data, and requests that mention "可视化仪表盘", "数据可视化", "矩形树图", "treemap", "数据看板", "报表可视化", "Excel 做成看板", "业务数据大屏", "交互式数据大屏", "数据观察仪表盘", "高密度可视化", "沿用矩形树图数据观察仪表盘方法论", "自动获取数据并可视化", or "把这些数据做成交互网页".
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.