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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skylv-context-aware-scheduler
by LeoYeAIContext-aware task scheduling with priority management
plan-generator
by aipochAutomatically generates a Markdown final-exam review plan or lab experiment schedule when you provide a date range, tasks/items, and available daily hours (via interactive prompts or a one-time JSON input).
float
by freekmurzePlan and manage team allocations on Float.com using the Float API. Use when the user wants to plan someone on a project, check who is planned where, view allocations, create or update allocations, look up people or projects, or anything related to Float resource planning. Triggers on phrases like "plan", "allocate", "schedule someone on a project", "who is working on", "Float", or any reference to team resource planning.
auto-task
by yunshu0909复杂长程任务的自主执行流程。当用户有一个复杂或模糊的任务（"帮我搞清楚 X / 帮我评估 Y / 帮我把这堆东西整理出来 / 帮我对比 N 个方案 / 帮我跑一次调研"），希望 AI 自己拆解、自己执行、自己校验、只在关键时刻找用户的场景。通过"任务确认 → 任务队列 → 分批执行 → 周期校验队列 → 触发式汇报"实现 1-2 小时无人值守的自主执行。当用户说"帮我搞清楚 / 评估一下 / 整理一下 / 对比一下 / 跑一次调研 / 你自己跑别打扰我 / 长程任务 / 自主跑"时触发。**不适用于**：UI 设计（用 design-exploration）、待办优先级（用 priority-judge）、文章写作（用 writing-assistant）、需求池管理（用 backlog-manager）、终局发散（用 vision-exploration）、起名（用 product-naming）、有明确 spec 的实现编码任务（直接编码）。
fleet-state
by aaronjmarsWeekly fleet-state digest — synthesises fork-cohort, contributor-spotlight, and fork-release-tracker into one "state of the fleet" narrative
task-tracker
by bkywksj任务跟踪与进度管理技能，管理开发任务的创建、分解、状态更新和归档。 触发场景： - 用户需要创建或查看开发任务 - 用户需要更新任务进度或状态 - 用户需要归档已完成的任务 触发词：任务、进度、待办、TODO、跟踪
feishu-task
by OPPO-Mente-LabFeishu task management operations. Activate when user mentions tasks, todos, task lists.
goal-progress-anchor
by Peiiii当复杂任务会跨多轮推进、容易偏航、容易遗忘原始目标或在讨论中逐步滑向新问题时使用。通过一个极短的 goal-progress 文件和回复计数器，强制周期性重新对齐目标、边界与下一步。
schedule
by potetoOrders scheduler. Reads .noodle/mise.json, writes .noodle/orders-next.json. Schedules work orders based on backlog state, plan phases, session history, and task type schedules. Triggers when orders are empty, after backlog changes, when the loop re-evaluates, or when /schedule is invoked.
ab-resume-task
by ayoubben18Continue an existing task from its progress tracker. Use when the user wants to resume, continue, or pick up earlier task work.
fizzy
by lyloInteract with the Fizzy tracker for Pagecord work. Use when the user asks to list, create, update, move, close, comment on, or inspect Fizzy cards or boards.
shopify-admin-order-notes-and-attributes-report
by 40RTY-aiRead-only: extracts and tabulates order notes and custom attributes for ops review of gift messages and special instructions.
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.