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...
zdoc-i2i
by zengle22设计文档到实施任务转化引擎。输入 15+ 种设计文档（PRD/Arch/API/UX/Tech/Test/Data/DDD/...），源文档交叉一致性校验 → 内容整合 → 按最小可验收颗粒度拆分 Task → 交付前自动审核 → 产出独立 impl 文档 + 依赖 DAG + 交付报告。纯 LLM + 结构化输出架构。
zcode-patrol
by zengle22定期对代码库进行自动化巡检，发现常见代码问题（风格漂移、架构腐化、安全漏洞、性能陷阱、重复代码等），生成结构化报告并推动修复，阻止代码熵增。
zcode-review-deep
by zengle22多智能体深度代码审查技能。对单次 Commit/PR/模块执行 4+ 维度并行专项审查，合并去重后生成结构化修复任务与最终报告。
zcode-safe-dev
by zengle22临时安全编码助手 - 修改 LEE 项目代码时的安全约束与自检清单
zdoc-design-check
by zengle22Pre-SSOT 文档校验技能。6 大维度（商业/产品/UX/架构/测试/工程）+ 跨维度一致性，共 55 项检查。纯 LLM + 结构化输出架构，产出 BLOCK/WARN/PASS 诊断报告。
zdoc-quality-loop
by zengle22多文档质量收敛流水线。Review 阶段采用 BMAD 多角色评审团（选角→并行评审→合并→讨论→共识/升级人类）。Fix/Verify 阶段不变。过程审计和结果报告由独立 Agent 从磁盘重建，无共享上下文。
zdoc-write
by zengle22标准文档撰写引擎。根据用户输入撰写 16 种 SSOT 文档（PRD/Arch/API/UX/Tech/Test/Data/DDD/Skill/Adapter/Job/Business/Strategy/Review/Testset/UX-Prototype），自动填充必输章节、补充缺失内容、识别核心决策点和分歧点供用户确认，严格遵循 DOC-WRITING-GUIDE 和 ITERATION-DOCUMENT-CHECKLIST 完整性要求。
zgsd-plan-phase
by zengle22Bridge impl task packs to GSD PLAN format for deterministic phase planning. Trigger when user says "plan phase", "bridge task pack", or invokes /zgsd-plan-phase.
zgsd-bootstrap-milestone
by zengle22Bootstrap a GSD milestone from a pre-designed document package with admission gating. Use when the user has a complete design document package (PRD, UX spec, Tech Design, Implementation Scope, etc.) and wants to convert it directly into GSD planning artifacts (milestone, requirements, roadmap, phase contexts) without going through GSD's discovery/questioning phase. Triggers on: 'bootstrap milestone from docs', 'create milestone from design package', 'import design docs as milestone', 'convert docs to GSD milestone'.
ll-dev-feat-to-proto
by zengle22Governed workflow skill for transforming one frozen FEAT into a static HTML interactive prototype package with complete journey flow, UX button responses, and structured human review gating before UI spec derivation.
ll-dev-feat-to-surface-map
by zengle22Governed workflow skill for transforming one frozen FEAT inside a feat_freeze_package into a surface_map_package that binds design ownership before downstream tech, proto, ui, and impl derivation.
ll-dev-feat-to-tech
by zengle22Governed LL workflow skill for transforming one frozen FEAT inside a feat_freeze_package into a TECH-first design package with optional ARCH and API companions before downstream tech-impl work.
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.