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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xlsx
by JS-markComprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When 小跃 needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
changelog-gen
by JS-markThis skill should be used when the user asks to "生成 changelog", "generate changelog", "release notes", "发布说明", "版本日志", "变更记录", "更新日志", "what changed", "总结变更", "prepare release", or when the user needs to generate structured release notes from git history.
code-reviewer
by JS-mark审查代码质量，发现潜在问题并给出改进建议
commit-helper
by JS-markThis skill should be used when the user asks to "提交代码", "commit", "写 commit message", "生成提交信息", "规范提交", "smart commit", "帮我提交", "提交变更", or when the user has finished making changes and wants to create a standardized git commit.
dev-standards
by JS-markThis skill should be used when the user asks to "检查代码规范", "check standards", "review code standards", "代码是否符合规范", "规范检查", "check my code", "does this follow our conventions", "coding standards", "开发规范", or provides code and asks if it follows the project's coding standards.
drama-writer
by JS-markThis skill should be used when the user asks to "写短剧", "写剧本", "短剧创作", "分集大纲", "下一集", "继续写剧本", "写分镜", "角色设计", "成本预估", "写台词", "剧本格式", "竖屏短剧", "横屏微短剧", or discusses drama script creation, episode outlines, storyboard generation, short drama series workflows, or screenplay writing.
feature-planner
by JS-markThis skill should be used when the user asks to "规划功能", "plan feature", "新功能开发", "功能设计", "feature design", "拆解任务", "todo list", "实现方案", "开发计划", "怎么实现", "how to implement", or when the user describes a new feature they want to build and needs a structured implementation plan with TODO breakdown.
i18n-helper
by JS-markThis skill should be used when the user asks to "做多语言", "国际化", "i18n", "翻译文本", "多语言化", "add translations", "internationalize", "i18nify", "替换硬编码文本", "把文本改成多语言", "add i18n", "抽取翻译", "extract translations", or when the user provides code/files containing hardcoded Chinese or English UI text that should be replaced with i18n translation calls.
iconfont-downloader
by JS-markThis skill should be used when the user asks to "下载图标", "搜索图标", "iconfont", "download icon", "search icon", "找个图标", or when the user needs to search and download SVG icons from iconfont.cn.
log-analyzer
by JS-markThis skill should be used when the user asks to "分析日志", "analyze logs", "排查问题", "debug error", "看看这个报错", "错误排查", "日志分析", "为什么报错", "what went wrong", "troubleshoot", "crash analysis", "定位问题", or when the user pastes error logs, stack traces, or console output and needs diagnosis.
novel-writer
by JS-markThis skill should be used when the user asks to "写小说", "创作小说", "写长篇", "继续写", "更新文章", "写大纲", "设计人物", "写角色", "下一章", "展开写", "帮我写故事", "搜索素材", "生成插图", or discusses novel creation, story outlines, character design, chapter writing, serial fiction workflows, or novel illustration.
pr-reviewer
by JS-markThis skill should be used when the user asks to "审查 PR", "review PR", "review pull request", "代码审查", "review changes", "检查变更", "看看改了什么", "审查代码", "check my changes", "review diff", or when the user wants a project-specific code review of their pending changes focusing on architecture compliance, standards, and security.
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.