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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ios-application-dev
by zhizhunbaoiOS application development guide covering UIKit, SnapKit, and SwiftUI. Includes touch targets, safe areas, navigation patterns, Dynamic Type, Dark Mode, accessibility, collection views, common UI components, and SwiftUI design guidelines. For detailed references on specific topics, see the reference files. Use when: developing iOS apps, implementing UI, reviewing iOS code, working with UIKit/SnapKit/SwiftUI layouts, building iPhone interfaces, Swift mobile development, Apple HIG compliance, iOS accessibility implementation.
zenstudio
by zhizhunbaoZenStudio 官方 AI 内容创作 CLI 工具 (zencli)。支持 AI 生图、AI 生视频、项目管理、资产库、媒资管理、无限画布、文件上传下载等。Use when user asks to generate images, generate videos, manage projects, upload files, download assets, manage materials, or interact with ZenStudio platform via command line.
tencentcloud-cos
by zhizhunbao腾讯云对象存储(COS)和数据万象(CI)集成技能。当用户需要上传、下载、管理云存储文件，或需要进行图片处理（质量评估、超分辨率、抠图、二维码识别、水印）、智能图片搜索、文档转PDF、视频智能封面生成等操作时使用此技能。
wechat-miniprogram
by zhizhunbaoWeChat Mini Program (微信小程序) development framework. Use when building WeChat mini apps with WXML templates, WXSS styles, WXS scripting, component development, WeChat API integration, cloud development (云开发), and Mini Program lifecycle/performance optimization.
tencent-ssv-techforgood
by zhizhunbao专注中国公益慈善领域的智能助手，精通公益机构数字化赋能、慈善合规咨询、社会救助引导和公益法规解读。擅长从腾讯技术公益数字工具箱（techforgood.qq.com）精准匹配免费工具，通过交互式引导帮助公益机构实现数字化转型。当用户提到公益、慈善、NGO、社会组织、公益机构数字化、技术公益、公益虾时使用。
lbo-model
by zhizhunbaoThis skill should be used when completing LBO (Leveraged Buyout) model templates in Excel for private equity transactions, deal materials, or investment committee presentations. The skill fills in formulas, validates calculations, and ensures professional formatting standards that adapt to any template structure.
fbs-bookwriter
by zhizhunbao福帮手出品|中文人机协同著书与长文档：书籍、企业/培训手册、行业白皮书与指南；S0–S6 工作流、强制联网查证、S/P/C/B 分层审校、中文排版与 MD/HTML 交付。 触发词（精选）：写书、写长篇、写手册、写白皮书、写行业指南、协同写书、定大纲、写章节、排版构建、导出、去AI味、质量自检、图文书。
agent-mbti
by zhizhunbaoAI Agent personality diagnosis and configuration system based on MBTI framework. Use when users want to (1) test/diagnose an Agent's personality type, (2) understand the gap between Agent's actual personality and user's desired personality, (3) generate configuration recommendations to adjust Agent behavior, (4) customize Agent's communication style, proactivity, reasoning approach, or execution patterns. Supports both free tier (quick assessment) and premium tier (full 93-question assessment with detailed diagnostics).
canvas-design
by zhizhunbaoCreate beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
citation-manager
by zhizhunbaoAdd real references and standardize citations for research papers and theses. Supports CrossRef integration, multiple citation formats (APA/MLA/Chicago/GB-T), batch import, and auto-detection of citation needs.
goal-tracker
by zhizhunbaoTrack long-term goals with milestones, daily logging, and accountability. Use when users want to set goals, log daily progress, update milestones, generate weekly summaries, or view an HTML dashboard of their goal progress.
habit-tracker
by zhizhunbaoBuild habits with streaks, reminders, and progress visualization. Use when users want to create daily/weekly habits, track streaks, set reminders, or view habit progress over time.
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.