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.
Querying local SQLite index...
project-os
by PeiiiiAI project OS for autonomous loop, automated orchestration, and rule-driven execution.
qq-group-speaker-distinction
by PeiiiiUse when integrating QQ group chat where users want one shared group session, but the assistant must still distinguish who said each message.
qq-url-guard
by PeiiiiUse when sending replies to QQ where URL-like text (for example xx.xx, USER.md, markdown links, or http URLs) may trigger code 40034028 and get blocked.
x-twitter-bird
by PeiiiiUse when the user wants to read bookmarks, likes, threads, search X/Twitter, or draft/post/reply through bird CLI with reusable local credentials stored on this machine.
goal-progress-anchor
by Peiiii当复杂任务会跨多轮推进、容易偏航、容易遗忘原始目标或在讨论中逐步滑向新问题时使用。通过一个极短的 goal-progress 文件和回复计数器，强制周期性重新对齐目标、边界与下一步。
desktop-release-contract-guard
by PeiiiiUse when building, verifying, or releasing NextClaw desktop installers, DMGs, update bundles, update manifests, or one-command desktop beta/stable release automation. Enforces the packaged update public key contract, the required verification commands, and the rule that raw electron-builder output is not enough.
linear-cli
by PeiiiiUse when the user wants to list, view, start, create, or update Linear issues from the terminal via schpet/linear-cli, including setup, auth, repo config, and safe read/write boundaries.
automation-setup
by PeiiiiTurn a user request into a scheduled automation by configuring cron jobs, including session-bound follow-ups and periodic checks.
aigen-image-generation
by PeiiiiUse the local aigen CLI to generate images through configured providers such as OpenRouter or OpenAI. Use when the user asks to create, generate, or render images and wants files produced locally through an external CLI.
kernel-branch-owner-architecture
by Peiiii当设计或重构主干/分支架构、kernel、desktop main、runtime host、presenter-manager-store、manager/store/presenter、生命周期装配、owner 依赖、贡献点 contribution，或用户指出最小传参、上层代读、factory/create/registry 过度抽象、稳定业务依赖被过度解耦、prop 透传或链路过长时使用。
mmx-cli
by PeiiiiUse mmx to generate text, images, video, speech, and music via the MiniMax AI platform. Use when the user wants to create media content, chat with MiniMax models, perform web search, or manage MiniMax API resources from the terminal.
mvp-view-logic-decoupling
by Peiiii当设计或重构前端 MVP / presenter-manager-store 架构、Zustand store / persist、前端状态归属、view-logic 解耦、页面刷新状态恢复、localStorage/sessionStorage 替代、减少 prop drilling、业务组件内聚、自包含业务容器、业务编排层、多组件状态/动作协调、复杂 React hook/component 状态机、streaming/data-flow 协调或 RxJS 评估时使用。
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.