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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higress-openclaw-integration
by higress-groupDeploy and configure Higress AI Gateway for OpenClaw integration. Use when: (1) User wants to deploy Higress AI Gateway, (2) User wants to configure OpenClaw to use more model providers, (3) User mentions 'higress', 'ai gateway', 'model gateway', 'AI网关', (4) User wants to set up model routing or auto-routing, (5) User needs to manage LLM provider API keys.
higress-auto-router
by higress-groupConfigure automatic model routing using the get-ai-gateway.sh CLI tool for Higress AI Gateway. Use when: (1) User wants to configure automatic model routing, (2) User mentions 'route to', 'switch model', 'use model when', 'auto routing', (3) User describes scenarios that should trigger specific models, (4) User wants to add, list, or remove routing rules.
higress-daily-report
by higress-group生成 Higress 项目每日报告，追踪 issue/PR 动态，沉淀问题处理经验，驱动社区问题闭环。用于生成日报、跟进 issue、记录解决方案。
higress-wasm-go-plugin
by higress-groupDevelop Higress WASM plugins using Go 1.24+. Use when creating, modifying, or debugging Higress gateway plugins for HTTP request/response processing, external service calls, Redis integration, or custom gateway logic.
nginx-to-higress-migration
by higress-groupMigrate from ingress-nginx to Higress in Kubernetes environments. Use when (1) analyzing existing ingress-nginx setup (2) reading nginx Ingress resources and ConfigMaps (3) installing Higress via helm with proper ingressClass (4) identifying unsupported nginx annotations (5) generating WASM plugins for nginx snippets/advanced features (6) building and deploying custom plugins to image registry. Supports full migration workflow with compatibility analysis and plugin generation.
agent-session-monitor
by higress-groupReal-time agent conversation monitoring - monitors Higress access logs, aggregates conversations by session, tracks token usage. Supports web interface for viewing complete conversation history and costs. Use when users ask about current session token consumption, conversation history, or cost statistics.
harness-creator
by higress-groupDesign and create AI-agent infrastructure for codebases: AGENTS.md, documentation architecture (docs/), linters with actionable errors (scripts/lint-*), harness/ configs, and CI integration. Creates files directly — never writes business/application code.
harness-executor
by higress-groupExecute development tasks autonomously with self-validation. Auto-bootstraps harness via harness-creator if missing. Use when the user asks to implement features, fix bugs, refactor code, execute plans, or make any code change in an existing or new codebase.
create-issue-himarket
by higress-group通过自然语言在 HiMarket 社区创建 Issue。支持 Feature Request（功能请求）和 Bug Report（问题报告）两种类型。当用户想要向 HiMarket 提交功能建议或报告问题时使用此 skill。
create-pr-himarket
by higress-group为 HiMarket 项目创建符合规范的 Pull Request。当用户需要提交代码、推送分支或创建 PR 时使用此 skill，确保 PR 标题和内容符合项目 CI 检查要求。
frontend-coding-standards
by higress-groupHiMarket 前端（React + TypeScript + Vite）代码规范：Prettier/ESLint、严格 TypeScript、注释与 Fast Refresh、Design Token、Tailwind、验证命令。
github-issue-briefing
by higress-group从 GitHub Issues 收集用户反馈并生成简报。抓取 higress-group/himarket 的 issues，按类型分类、去重（排除已处理的重复内容），生成包含趋势分析和优先级建议的简报。当用户想了解社区反馈、issue 概况、用户需求趋势时使用此 skill。
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.