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...
planning-with-files-ar
by keeganmoody33نظام تخطيط الملفات بنمط Manus لتنظيم وتتبع تقدم المهام المعقدة. ينشئ ملفات task_plan.md و findings.md و progress.md. يُستخدم عند طلب التخطيط أو تحليل المهام أو تنظيم المشاريع أو تتبع التقدم أو الخطط متعددة الخطوات. يدعم الاستعادة التلقائية للجلسة بعد /clear. كلمات التشغيل: تخطيط المهام، إدارة المشاريع، خطة العمل، تحليل المهام، تنظيم المشروع، تتبع التقدم، خطة متعددة الخطوات، ساعدني في التخطيط، تحليل المشروع
planning-with-files-de
by keeganmoody33Manus-artiges Dateiplanungssystem zur Organisation und Verfolgung des Fortschritts komplexer Aufgaben. Erstellt task_plan.md, findings.md und progress.md. Wird verwendet, wenn der Benutzer plant, zerlegt oder organisiert: mehrstufige Projekte, Forschungsaufgaben oder Arbeiten mit über 5 Tool-Aufrufen. Unterstützt automatische Sitzungswiederherstellung nach /clear. Auslöser: Aufgabenplanung, Projektplanung, Arbeitsplan erstellen, Aufgaben analysieren, Projekt organisieren, Fortschritt verfolgen, Mehrstufige Planung, Hilf mir bei der Planung, Projekt zerlegen
planning-with-files-es
by keeganmoody33Sistema de planificación basado en archivos estilo Manus para organizar y rastrear el progreso de tareas complejas. Crea task_plan.md, findings.md y progress.md. Cuando el usuario solicita planificación, desglose u organización de proyectos multipaso, tareas de investigación o trabajos que requieren más de 5 llamadas a herramientas. Soporta recuperación automática de sesión tras /clear. Palabras clave: planificación de tareas, planificación de proyecto, crear plan de trabajo, analizar tareas, organizar proyecto, seguimiento de progreso, planificación multipaso, ayúdame a planificar, desglosar proyecto
planning-with-files-zht
by keeganmoody33基於 Manus 風格的檔案規劃系統，用於組織和追蹤複雜任務的進度。建立 task_plan.md、findings.md 和 progress.md 三個檔案。當使用者要求規劃、拆解或組織多步驟專案、研究任務或需要超過5次工具呼叫的工作時使用。支援 /clear 後的自動會話恢復。觸發詞：任務規劃、專案計畫、制定計畫、分解任務、多步驟規劃、進度追蹤、檔案規劃、幫我規劃、拆解專案
canonical-docs-system
by keeganmoody33Enforces a documentation-first workflow by interrogating requirements, drafting the six canonical docs (PRD, APP_FLOW, TECH_STACK, FRONTEND_GUIDELINES, BACKEND_STRUCTURE, IMPLEMENTATION_PLAN), cross-referencing them, and maintaining CLAUDE.md/progress.txt. Use when starting a project, creating or refreshing canonical docs, or turning a plan into authoritative documentation.
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.