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...
crater-cli-auth
by raids-labCrater CLI 认证域：指导 AI Agent 帮用户登录、重新登录、查看和切换已保存身份、删除凭据、登出当前身份，以及排查 token、session、active_context、Keyring、401/403、未登录等认证问题。用户提到 crater auth、login、logout、switch、ls、rm、session、token、active context、Keyring、未登录、认证失败、权限错误时使用。
crater-cli-shared
by raids-labCrater CLI 共享基础：安全调用 crater 命令的通用规则，包括可执行文件选择、全局选项、--json、--no-interactive、--help、错误输出、退出码、敏感信息处理，以及执行会修改用户环境的命令前的确认规则。处理任何 Crater CLI 操作前使用。
crater-cli-config
by raids-labCrater CLI 配置域：指导 AI Agent 帮用户查看和修改 CLI 本地配置，当前重点支持显示语言切换。用户提到 crater config、language、语言、中文、英文、切换语言、显示语言、配置项、state.json 时使用。
crater-cli-completion
by raids-labCrater CLI 补全域：指导 AI Agent 帮用户生成、安装、更新或卸载 bash/zsh Tab 补全脚本，并排查 shell 补全不可用问题。用户提到 crater completion、crater comp、Tab 补全、bash、zsh、.bashrc、.zshrc、补全脚本、completion install/uninstall 时使用。
crater-devel-code
by raids-labCrater 代码开发：在 backend/、frontend/、cli/ 下开发 Go 后端、React 前端、CLI、API、作业模板、组件、表单、hooks、i18n 与测试。用户修改 backend/、frontend/、cli/ 代码或前后端/CLI 联动时使用；调用 crater 命令则用 cli/skills/crater-cli-*；开始前须应用 crater-devel-shared。
crater-devel-release
by raids-labCrater 发布（开发者侧）：charts/ Helm Chart 开发与版本管理、values/README 同步，以及经 CI 产出的镜像与 Chart 发布物。用户修改 charts/、Helm 模板/values、Chart 版本或准备发布产物时使用；开始前须应用 crater-devel-shared。运维内部集群（部署/rollout/重启/镜像 digest 校验）面向集群管理员，不属于本 Skill。
crater-devel-review
by raids-labCrater 全仓库代码审查与 PR 描述：纵览 backend/frontend/cli/charts/docs/website 的变更，按核心规范/优化建议分级反馈，并生成双语 PR 描述。用户要求审查 PR、diff、变更或撰写 PR 描述时使用；开始前须应用 crater-devel-shared。
crater-devel-shared
by raids-labCrater monorepo 开发共享基础：仓库结构、文档权威来源、跨模块任务路由与 Agent 全局行为。处理任何 Crater 仓库开发任务前使用；并据此选择应加载的 crater-devel-<domain> Skill。
crater-devel-docs
by raids-labCrater 文档开发：在 website/、docs/ 及仓库各级 Markdown 中维护平台用户文档、开发者文档、i18n、术语与 Chart 版本占位。用户修改文档、文档站、多语言文档或文档规范时使用；开始前须应用 crater-devel-shared。
crater-cli-admin-read
by raids-labCrater CLI 管理员只读域：指导 AI Agent 通过 crater admin ... 查看平台级只读信息。仅当用户明确要求管理员或平台级资源时使用。
crater-cli-download
by raids-labCrater CLI 下载域：指导 AI Agent 通过 crater download 创建、等待、查看、暂停、恢复、重试、删除模型和数据集下载任务，并安全处理 Hugging Face / ModelScope token。用户提到 crater download、模型下载、数据集下载、ModelScope、Hugging Face、hf、ms、下载日志、暂停/恢复/重试下载时使用。
crater-cli-read
by raids-labCrater CLI 用户视图读取域：指导 AI Agent 通过 crater node、job、image、account、resource、dataset、model-download、pod 等用户可见命令查看平台只读信息。管理员视图请使用 crater-cli-admin-read。
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.