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...
xlsx-offline
by okwindsExcel 表格离线读写与公式校验：创建/修改 xlsx，保持公式可复算，输出必须零公式错误；附带 LibreOffice 重算与错误扫描脚本（依赖安装可能需要网络）。
bf-caprt-dev
by okwinds指导编码智能体以 capability-runtime 为业务落地入口，交付基于 capability-runtime 的 skills / agents / workflows，并在 Greenfield 或 Legacy Convergence 场景下优先使用 Runtime public surface、structured output、NodeReport、host summary 与 service/session surfaces。只要任务目标是用 capability-runtime / capability_runtime 落地业务代码、收敛下游 runtime boundary，或涉及 Runtime.run / Runtime.run_stream / run_structured / run_structured_stream / AgentSpec / PromptRenderMode / prompt_render_mode / _runtime_prompt / precomposed_messages / multimodal / vision / image input / 多图输入 / 视频抽帧输入 / OpenAI-compatible messages / image_url content parts / WorkflowSpec / NodeReport / RuntimeServiceFacade / describe_capability / summarize_host_run，就应优先使用本技能。不要用于普通通用编码、prompt-only 任务、直接学习上游原生框架 API，或任何明确要求“直接用 skills-runtime-sdk / Agently / provider SDK，不走 capability-runtime”的任务；若已触发但随后识别出这是反目标，必须立即退出，并停止提供任何上游实现细节、伪代码或 API 猜测。
bf-skillsruntime-dev
by okwinds用 Skills Runtime SDK（Python）开发复杂业务 agent、skills、workflow 的编码智能体指南。用户一旦提到 skills_runtime、Skills Runtime SDK、overlay YAML、FakeChatBackend、AgentBuilder、Coordinator、skill_ref_read、skill_exec、approvals/sandbox、WAL/replay、exec sessions、spawn_agent/send_input/wait、waiting_human/resume、examples/apps/workflows，或要在本仓上落地复杂业务开发/修复/回归，就应优先使用本技能。不要用于与本框架无关的通用编码或纯文案任务。
loopback
by okwindsLoopback 迭代开发循环 - 基于 Ralph Loop 思想实现的 Codex 版本。用于创建自引用的迭代开发循环，让 AI 在多次迭代中逐步改进代码，直到任务完成。Use when: (1) 需要多次迭代改进的任务, (2) 复杂功能需要分步实现, (3) 需要自我修正的代码生成, (4) 迭代优化已有代码.
docx-offline
by okwindsDOCX 文档离线读写：提取/分析、OOXML 解包编辑回包、批注与修订（tracked changes/redlining）。适用于合同/制度/论文等需要保留格式与修订痕迹的场景（依赖安装可能需要网络）。
prd-writing-guide
by okwindsWrite complete, unambiguous PRDs that development teams can implement without guesswork. Includes requirement discovery framework, structured documentation methodology, completeness checklists, and common pitfall avoidance. Use when: writing new PRDs, reviewing PRD drafts, validating requirement completeness, preparing for engineering handoff. Triggers: 'write PRD', '写PRD', '产品需求文档', '需求文档', '需求规格', '需求评审', '完善需求', 'create requirements doc', 'product requirements', 'feature spec', 'requirements document'. Anti-triggers: 'technical design doc', 'architecture design', 'implementation plan', 'API design', '架构设计', '技术方案', '实现方案', '接口设计'.
incident-triager
by okwinds排障助手（人类应用示例）：读取日志并提出澄清问题。
data-qa-reporter
by okwinds数据 QA 与报告（人类应用示例）：shell_exec 校验并输出 report.md。
repo-qa-runner
by okwindsRepo 流水线：运行最小回归（pytest）。
repo-reporter
by okwinds报告生成：汇总 Review/Fix/QA 的结果并写 report.md（workflow 示例：Reporter 角色）。
reporter
by okwinds汇总报告：把本次 workflow 的关键证据与产物路径写入 report.md。
runbook-writer
by okwinds排障助手（人类应用示例）：生成可执行的 runbook，并写入 workspace。
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.