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...
slang-shader-engineer
by linyute在處理 Slang 著色器、著色器模組、HLSL 相容的 GPU 程式碼、繪圖管線、運算著色器、鑲嵌 (tessellation)、光線追蹤、參數區塊、泛型、介面、capabilities、交叉編譯、著色器最佳化、著色器檢閱或 Slang 的 C++ 引擎整合時使用。當提到 Slang、.slang 檔案、slangc、來自 Slang 的 SPIR-V、Slang 模組、[shader("compute")]、[shader("vertex")] 或要求使用現代語言特性編寫/檢閱/重構著色器程式碼時觸發。當出現 Slang 轉 HLSL/GLSL/Metal/CUDA 的交叉編譯問題，或者使用者在提到「著色器」的同時提到「泛型」、「介面」、「參數區塊」、「自動微分 (autodiff)」或「capabilities」時也會觸發。
dotnet-mcp-builder
by linyute使用目前的 ModelContextProtocol 1.x NuGet 套件在 C#/.NET 中建構 Model Context Protocol (MCP) 伺服器。特別有助於處理模型在沒有指引的情況下常出錯的情況 — 過時的預覽版本（模型傾向於選擇 0.3 或 0.4 預覽版）、MCP 應用程式（在主機中呈現的互動式 UI）、elicitation URL 模式、每個工作階段的 HTTP 佈線、OAuth 和反向代理部署細節，以及偵錯具體的 MapMcp / STDIO / Streamable-HTTP 錯誤。同時也涵蓋了常規工作 — STDIO 和 Streamable HTTP 傳輸（SSE 已過時）、工具、提示、資源、取樣、根、自動補全、記錄 — 以及基本的 .NET MCP 用戶端。當使用者提到或暗示任何 .NET MCP 伺服器工作時觸發：ModelContextProtocol, McpServerTool, MapMcp, WithStdioServerTransport, "MCP server in C#", "MCP tool in dotnet", "expose this as MCP"，或在 .NET 上下文中命名一個基元（提示/資源/elicitation/MCP 應用程式）。對於其他語言的 MCP 工作請跳過。
go-mcp-server-generator
by linyute使用官方的 github.com/modelcontextprotocol/go-sdk 建立一個具有適當結構、依賴項和實作的完整 Go MCP 伺服器專案。
kotlin-mcp-server-generator
by linyute使用官方 io.modelcontextprotocol:kotlin-sdk 函式庫，產生一個具有適當結構、依賴項和實作的完整 Kotlin MCP 伺服器專案。
csharp-nunit
by linyute取得 NUnit 單元測試最佳實踐，包括資料驅動測試
python-pypi-package-builder
by linyute這是一個端對端的技能，用於建構、測試、檢查、版本控制並發佈生產級別的 Python 函式庫到 PyPI。涵蓋所有四種建構後端 (setuptools+setuptools_scm, hatchling, flit, poetry)、PEP 440 版本控制、語義化版本、動態 git 標籤版本控制、OOP/SOLID 設計、型別提示 (PEP 484/526/544/561)、受信任的發佈 (OIDC) 以及完整的 PyPA 封裝流程。適用於：建立 Python 套件、可透過 pip 安裝的 SDK、CLI 工具、框架外掛程式、pyproject.toml 設定、py.typed、setuptools_scm、semver、mypy、pre-commit、GitHub Actions CI/CD 或 PyPI 發佈。
penpot-uiux-design
by linyute使用 MCP 工具在 Penpot 中建立專業 UI/UX 設計的全面指南。當執行以下操作時使用此技能：(1) 為網頁、行動裝置或桌面應用程式建立新的 UI/UX 設計，(2) 使用元件和權杖 (Tokens) 建構設計系統，(3) 設計儀表板、表單、導覽或登陸頁面，(4) 套用無障礙標準和最佳實踐，(5) 遵循平台指南 (iOS, Android, Material Design)，(6) 審查或改進現有的 Penpot 設計以提高可用性。觸發詞：「設計 UI」、「建立介面」、「建構佈局」、「設計儀表板」、「建立表單」、「設計登陸頁面」、「使其具備無障礙性」、「設計系統」、「元件函式庫」。
arize-ai-provider-integration
by linyute建立、讀取、更新與刪除 Arize AI 整合，儲存供評估者與其他 Arize 功能使用的 LLM 提供者認證。支援任何 LLM 提供者（例如 OpenAI、Anthropic、Azure OpenAI、AWS Bedrock、Vertex AI、Gemini、NVIDIA NIM）。當使用者提到 AI 整合、LLM 提供者認證、建立整合、列出整合、更新認證、刪除整合或將 LLM 提供者連接到 Arize 時使用。
x-twitter-scraper
by linyute使用 Xquik X API SDK、REST 端點、MCP 工具、已簽署的 Webhook、推文搜尋、使用者查找、跟隨者匯出、媒體操作和代理程式自動化來建構 GitHub Copilot 工作流程。
eyeball
by linyute具備內建原始資料螢幕截圖的文件分析。當您要求 Copilot 分析文件時，Eyeball 會產生一個 Word 文件，其中每項事實主張都包含原始資料的反白顯示螢幕截圖，讓您可以親眼驗證。
doublecheck
by linyute三層驗證管線，適用於 AI 輸出。擷取可驗證的主張，透過網路搜尋尋找支持或矛盾的來源，針對幻覺模式執行對抗性審核，並產生包含供人工審核之來源連結的結構化驗證報告。
create-tldr-page
by linyute從文件 URL 和命令範例建立 tldr 頁面，需要 URL 和命令名稱。
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.