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...
anysearch
by sundanian1991实时搜索引擎，支持通用网页搜索、23 个垂直领域搜索、并行批量搜索和 URL 内容提取。当用户需要搜索网络信息或提取网页内容时触发。
khazix-neat-freak
by sundanian1991会话结束后的知识库洁癖级清理 — 审查项目文档（CLAUDE.md、README.md、docs/）与 Agent 记忆，确保与代码/当前状态一致，无过期内容。触发词：同步、整理文档、整理记忆、收尾、这个阶段做完了、新人能直接上手。
viz-antv-s2-expert
by sundanian1991S2 multi-dimensional cross-analysis table development assistant (Expert Skill). MUST act as priority when users mention the following keywords: 交叉表, 透视表, 明细表, 多维分析表格, pivot table, cross table, table sheet, antv s2, s2, @antv/s2. Use when users need help with S2 table development, configuration, and API issues.
viz-antv-s2-expert
by sundanian1991S2多维交叉表分析助手。专治：交叉表、透视表、明细表、多维分析表格、pivot table、antv s2。触发于"交叉表"、"透视表"、"S2"、"@antv/s2"。
ggplot2
by sundanian1991R ggplot2 包使用指南，覆盖 4.0+ 新特性：S7 迁移、主题默认值、element_geom()、离散尺度改进、坐标反转等。适用于 R/ggplot2 图表开发场景。
ggplot2
by sundanian1991R ggplot2 包使用指南，覆盖 4.0+ 新特性：S7 迁移、主题默认值、element_geom()、离散尺度改进、坐标反转等。适用于 R/ggplot2 图表开发场景。
viz-narrative-text
by sundanian1991数据叙事文本可视化 — 使用 T8 Syntax 将数据转化为带语义标注的叙事报告。支持 18 种实体类型（指标/变化/趋势/异常/排名/季节性等），可内嵌迷你图。当用户说'做个数据报告'、'数据解读'、'业绩分析'、'市场研究报告'、'把数据写成文章形式'时触发。不适用于：纯表格、交叉分析表、数据图表（柱状/折线/环形）。
viz-narrative-text-visualization
by sundanian1991用 T8 语法生成结构化叙事文本可视化。适用于数据解读报告、摘要、带语义标注的结构化文章等需求。
deep-reading-analyst
by sundanian1991深度阅读分析 - 使用 10+ 思维模型（SCQA/5W2H/批判性思维）深度分析文章、论文
book-processor
by sundanian1991书籍深度处理 Skill
xlsx
by sundanian1991Excel 表格处理 - 读写/编辑/清理 .xlsx/.csv/.tsv，支持公式、图表、数据清理
doc-xlsx
by sundanian1991Excel 表格处理 - 创建/编辑/分析 spreadsheet，支持公式、格式、数据分析和可视化
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.