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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blink1
by czytUse when controlling blink(1) USB LED device via blink1-tool CLI, setting RGB/HSB colors, fade transitions, patterns, blinking effects, multi-device/LED control, or Linux udev setup. Keywords: blink1-tool, ThingM, USB LED, notification light, RGB color, HSB, pattern, blink, fade, chase, glimmer.
go-kratos
by czytGo-Kratos 微服务框架开发助手。触发词：kratos、protobuf API 定义、Go 微服务分层架构、HTTP/gRPC 服务配置、中间件开发、JWT/Casbin 认证、buf 生成工具、proto validate、WebSocket/文件上传。即使未明确提及 kratos，当涉及这些主题或项目包含 buf.yaml、internal/service、internal/biz 目录时也应触发。
media-search
by czyt搜索电影种子/磁力链接、搜索下载音乐专辑、管理 qBittorrent 下载任务。触发词：找资源、下载、磁力、种子、torrent、magnet、影视、音乐下载、qBt、搜电影、下电影、搜歌、下歌
golang-style
by czytUse this skill BEFORE writing or editing any Go (.go) files. Triggers when about to create, modify, or add code to .go files. Enforces happy path coding, error wrapping, sentinel errors, and godoc-style comments.
lazycat-sdk-dev
by czytLazyCat SDK 开发技能，用于 Go/JS 应用与微服务 API 交互（用户、设备、应用、设备控制）。包含前端 WebShell 能力（AppCommon、MediaSession、主题、导航）。触发词：@lazycatcloud/sdk 导入、lzc-sdk 引用、SDK/WebShell/设备查询。
lazycat-webshell-dev
by czytUse when developing or reviewing LazyCat/LightOS WebShell provider LPK apps, debugging provider discovery, launch URL, session restore, mobile terminal UX, lightos-admin return navigation, Catlink, lightosctl exec/forward, Publish API, or zellij/tmux/web terminal adapters. 触发词：开发WebShell provider、WebShell调试、LightOS终端、会话恢复、手机终端、lightosctl桥接。
rime-custom
by czytRime 输入法配置定制助手。支持 custom.yaml 覆写、Emoji/OpenCC、模糊拼音、Lua 扩展、多设备同步。覆盖雾凇/白霜/薄荷/万象方案。何时用：用户需要定制输入法配置、修改候选词数量、启用模糊拼音、配置Emoji、添加Lua扩展、导入词库、多设备同步时。触发词：Rime配置、小狼毫、鼠须管、custom.yaml、模糊拼音、辅助码、候选词、词库导入、Rime定制、输入法配置。
yzma
by czytUse this skill when working with yzma library for Go applications that integrate llama.cpp for local LLM inference. Triggers when creating, modifying, or debugging Go code that uses github.com/hybridgroup/yzma for language models, vision models, embeddings, or tool calling.
aur-github-publish
by czytAUR GitHub 发布助手 - 自动更新 PKGBUILD 版本、发布到 GitHub 仓库并同步到 AUR。支持首次发布引导（版本差异触发策略）、三种场景：版本监控更新、编译发布一体化（GitHub Action）、GoReleaser集成（aurs/aur_sources）。支持手动版本输入、自动版本检测、pkgrel bump、多架构支持（x86_64/aarch64）、deb/rpm/tar.gz 包处理。包含四种包类型命名规则（-bin/-git/无后缀/-font）。触发词：首次发布 AUR、创建新 AUR 包、更新 AUR 包、发布到 AUR、创建 PKGBUILD、更新 Arch Linux 包、AUR 自动发布、GoReleaser AUR。
d2lang
by czytUse when creating, validating, exporting, embedding, or automating D2/D2Lang diagrams, .d2 files, diagram-as-code architecture diagrams, CLI watch/render workflows, CI exports, or Go/Node programmatic diagram generation.
gemini-cli
by czyt当用户提到"用Gemini分析"、"看图"、"听录音"时触发。
go-portable-app
by czytGo portable application development agent for single-file deployment. Focuses on ent ORM + SQLite (entsqlite driver) + embed frontend + Wire/fx dependency injection. Use when developing Go apps with embedded resources, single-binary deployment, cross-platform compilation, or SQLite-based apps. Triggers: "portable go app", "单文件部署", "go embed", "entsqlite", "go portable", "嵌入式go应用", "single binary go", "cross compile go", "wire", "fx", "依赖注入".
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.