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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llm-wiki
by mduongvandinhXây dựng và duy trì knowledge base cá nhân theo pattern LLM Wiki (Karpathy). Hỗ trợ init, ingest, query, lint, discover, run, digest, pain-rank, setup, book-summary, competitive-brief, interview-prep.
engineering-failures-go
by mduongvandinhQuét mã nguồn Go tự động để phát hiện các mẫu lỗi kỹ thuật phổ biến. Dựa trên 140 patterns từ 12 lĩnh vực: Goroutine/Channel, Phân tán, Bảo mật, Dữ liệu, Tài nguyên, Interface/Thiết kế, Xử lý lỗi, Hiệu năng, API, Thử nghiệm, Triển khai, Giám sát. Chuyên biệt cho Go.
engineering-failures-java-springboot
by mduongvandinhQuét mã nguồn Java/Spring Boot tự động để phát hiện các mẫu lỗi kỹ thuật phổ biến. Dựa trên ~90 patterns từ 12 lĩnh vực: Bộ nhớ, Đồng thời, Bảo mật, Dữ liệu/JPA, Mạng, File I/O, Xử lý lỗi, Hiệu năng, API, Thử nghiệm, Triển khai, Giám sát. Chuyên biệt cho Java 21+ và Spring Boot 3.x.
engineering-failures-nodejs
by mduongvandinhQuét mã nguồn Node.js/TypeScript tự động để phát hiện các mẫu lỗi kỹ thuật phổ biến. Dựa trên 145 patterns từ 12 lĩnh vực: Event Loop/Async, Phân tán, Bảo mật Web, Dữ liệu, Tài nguyên, TypeScript, Xử lý lỗi, Hiệu năng, API, Thử nghiệm, NPM/Dependencies, Giám sát. Chuyên biệt cho Node.js/TypeScript.
engineering-failures-php
by mduongvandinhQuét mã nguồn PHP tự động để phát hiện các mẫu lỗi kỹ thuật phổ biến. Dựa trên 138 patterns từ 12 lĩnh vực: Kiểu dữ liệu, Bảo mật Web, Xác thực, Dữ liệu, Tài nguyên, Kiến trúc, Xử lý lỗi, Hiệu năng, API, Thử nghiệm, Triển khai, Giám sát. Chuyên biệt cho PHP (Laravel/Symfony).
engineering-failures-rust
by mduongvandinhQuét mã nguồn Rust tự động để phát hiện các mẫu lỗi kỹ thuật phổ biến. Dựa trên 142 patterns từ 12 lĩnh vực: Ownership/Borrowing, Async/Concurrency, Unsafe/FFI, Bảo mật, Bộ nhớ, Hệ thống kiểu, Xử lý lỗi, Hiệu năng, API/Crate, Thử nghiệm, Triển khai, Giám sát. Chuyên biệt cho Rust.
engineering-failures-dotnet
by mduongvandinhQuét mã nguồn .NET/C# tự động để phát hiện các mẫu lỗi kỹ thuật phổ biến. Dựa trên 143 patterns từ 12 lĩnh vực: Async/Task, Phân tán, Bảo mật, Entity Framework, Tài nguyên, Kiến trúc, Xử lý lỗi, Hiệu năng, API, Thử nghiệm, Triển khai, Giám sát. Chuyên biệt cho .NET/C#.
spring-boot-full-stack
by mduongvandinhComplete Java Spring Boot skill set for building enterprise applications. Includes modular architecture with optional components: - PostgreSQL database with JPA/Hibernate + Flyway migration - Redis caching (optional) - Kafka/RabbitMQ messaging (optional, choose one) - JWT + OAuth2 authentication (optional OAuth2) - RBAC authorization (optional) - TDD with Mockito - Spec-First Development with OpenSpec
spring-maven-modular
by mduongvandinhMaven Modular Architecture with profiles for optional components. Enable/disable modules like Redis, Kafka, RabbitMQ dynamically.
spring-tdd-mockito
by mduongvandinhTDD (Test-Driven Development) skill with Mockito for Spring Boot. Guides the Red-Green-Refactor cycle for writing tests first.
spring-openspec
by mduongvandinhSpec-First Development with OpenSpec for Spring Boot. Align on specifications before implementation begins.
repo-archaeologist
by mduongvandinhUse when setting up a new repo for TKS team, onboarding a legacy project, or when institutional knowledge is missing/outdated. Triggers: 'document this repo', 'new project setup', 'knowledge extraction', 'what does this codebase do', missing DOMAIN_MAP.md or TRAPS.md or DECISIONS_LOG.md in docs/institutional-memory/.
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.