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 stvlynnComprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude 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
xlsx
by stvlynnComprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude 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
12306-train-query
by stvlynn专业的12306火车票班次查询技能，支持准确的车站选择、日期指定和车次信息获取。使用已验证的浏览器操作SOP流程和URL参数直接查询策略，确保查询结果的准确性。适用于用户需要查询火车班次、余票信息、车次时间等12306相关查询场景。
Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.
dws
by stvlynn管理钉钉产品能力(AI表格/日历/通讯录/文档/机器人/待办/邮箱/听记/AI应用/审批/日志/钉盘等)。当用户需要操作表格数据、管理日程会议、查询通讯录、发送消息通知、处理审批流程、查看听记摘要、创建应用/系统/管理后台/业务工具、查看日报周报、管理钉盘文件时使用。
xiaohongshu
by stvlynn小红书搜索、发布、获取帖子详情。使用本地 MCP 服务器访问小红书内容，需要先登录。适用于搜索旅游攻略、美食推荐、获取帖子详情等场景。
atlassian-design
by stvlynnUse this skill whenever the user mentions Atlassian Design System, Atlaskit, Jira-style UI, Confluence-style UI, ADS components, design tokens, xcss, @atlaskit/css, primitives, navigation system, or wants to build/refactor React interfaces to match Atlassian patterns. This skill helps choose the right ADS component, combine multiple ADS patterns, and implement code grounded in the mirrored documentation under references/docs.
qwen-asr
by stvlynnSpeech-to-text using Qwen3-ASR-0.6B-4bit MLX model via a local FastAPI service. Transcribes audio files and URLs. Optimized for Apple Silicon. Use when user sends voice messages or audio that needs transcription.
create-sticker
by stvlynnGenerate LINE-style stickers of a character with background removal. Creates creative, unique poses with consistent character design via Google Gemini, removes solid background to transparent PNG. Use when user asks for sticker, 贴纸, LINE sticker.
tip-gui-skill
by stvlynnReuse local Youtu-Tip GUI capabilities through a safe adapter CLI so OpenClaw/Codex-style agents can inspect desktop GUI state and perform guarded single-step actions on macOS.
pv-tool
by stvlynnOperate the bundled PV Tool kinetic typography web app for lyric videos, promotional videos, and motion-graphics text overlays. Use this whenever the user mentions PV Tool, wants to run or preview the app locally, build it for distribution, experiment with PV templates and effects, or work with lyric text, media backgrounds, audio-reactive overlays, or LRC input inside PV Tool.
tsticker
by stvlynnManage Telegram sticker packs via tsticker CLI. Init, push, sync, download, and trace sticker packs. Use when user wants to create/update Telegram sticker packs, push stickers to Telegram, sync packs, or manage sticker collections. Integrates with create-sticker for end-to-end sticker generation → publish workflow.
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.