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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document-editing
by Nox-Lumen-tech二进制文档（DOCX/XLSX/PPTX）部分编辑：检索定位 → skill 编辑 → ES Copy-on-Write 快照
semgrep
by Nox-Lumen-tech跨语言 SAST 扫描，SQL 注入/XSS/密钥泄露/taint tracking，支持自定义 YAML 规则
cppcheck
by Nox-Lumen-techC/C++ Bug 检测，内存泄漏/空指针/数组越界/未定义行为，无需编译
detekt
by Nox-Lumen-techKotlin 静态分析，代码异味 + 复杂度 + 规范检查，内含 ktlint 规则集
docx
by Nox-Lumen-techUse this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a "report", "memo", "letter", "template", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
graft-comboagent
by Nox-Lumen-tech本地 agent 的 ragbase 远程客户端：直接查云端知识库（语义检索 / 列文档 / 翻 chunk）、嫁接已有 session 的执行成果，以及向已有 session 派发任务（dispatch）。
mypy
by Nox-Lumen-techPython 类型检查，检测类型不匹配/缺失注解/不兼容赋值，PEP 484 参考实现
pmd
by Nox-Lumen-techJava 源码反模式检测。识别冗余代码、未使用变量、空 catch、复杂度过高等问题， 含 CPD 拷贝粘贴检测（跨文件重复代码）。支持 Java/Kotlin。 规则配置可由 standards-converter Skill 从知识库编码手册自动生成， 或使用内置 quickstart 规则集。 触发："检查 Java 反模式"、"跑 PMD"、"检测重复代码"、 "Java 冗余/复杂度/空 catch 检查"。
ruff
by Nox-Lumen-techPython 极速 lint（替代 Flake8/Pylint/isort/Black），900+ 规则，Rust 编写
spotbugs
by Nox-Lumen-techJava 字节码级 Bug 检测 + FindSecBugs 安全扫描，检测空指针/资源泄漏/并发/OWASP
static-analysis
by Nox-Lumen-techL1 本地静态分析。调用 Checkstyle/PMD/SpotBugs/Detekt/Ruff/Bandit/Semgrep/cppcheck/clang-tidy 等工具链对代码做客观检测，输出 CodeEvidence JSON。支持 git diff、指定路径、--all 全量扫描三档。 触发词：静态分析、lint、跑检查工具、scan code、全量扫描、run linter、检查代码规范、安全扫描、跑 semgrep、检查漏洞、从头审、全分支审。 不触发：当用户说"review this PR"/"review before merge"/"is this safe?"时，走 code-review skill（LLM 主观审查）。 适用：需要对照需求文档/知识库的深度审核 → 先本地 L1 全量出 CodeEvidence → 上传云端 L2（详见 references/cloud-api-reference.md 的 "全量 + L2 组合"）。 与 code-review 的关系：code-review = LLM 主观评审（含 diff/全分支三种场景），static-analysis = 工具客观检测（需安装工具）。两者互补，可串联使用。
xlsx
by Nox-Lumen-techUse this skill whenever the user wants to create, read, edit, or manipulate Excel spreadsheets (.xlsx/.xls files). Triggers include: any mention of "Excel", "spreadsheet", ".xlsx", ".xls", or requests to produce tables, reports, dashboards with cell formatting, charts, or formulas. Also use when inserting images into spreadsheets, modifying cell values, adding/deleting rows or columns, formatting cells (fonts, colors, borders), working with multiple sheets, or generating data-driven reports as Excel files. Do NOT use for Word documents (.docx), PDFs, or general coding tasks unrelated to spreadsheet generation.
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.