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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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myagents-cli
by hAcKlyc你正在 MyAgents 这款 AI 产品里运行——MyAgents 自带一套"产品能力"（定时任务、任务中心、想法收集、MCP 工具接入、 模型 Provider、IM Bot 渠道、社区插件、Skills 安装、Generative UI Widget 等），全部通过内置 `myagents` CLI 暴露给你。 当用户的需求**落在 MyAgents 产品能力的射程内**，就加载并使用这个 skill，用 CLI 主动帮用户把事情做掉， 而不是让用户去 GUI 点击。 典型触发场景：用户说"每天 X 点帮我 Y"（→ cron）、"记一下这个想法"（→ thought）、"派发成任务"（→ task）、 "接个 X 工具进来"（→ mcp）、"配 X 模型/Provider"（→ model）、"在飞书/钉钉/Telegram 里跟我聊"（→ agent channel）、 "装个 X 插件 / 装个 X skill"（→ plugin / skill）、"把图发到 IM 里"（→ im send-media）、"做个图表/仪表盘" （→ widget readme）、"看下我有啥任务/定时/Runtime/版本"（→ list / status / version）、"改下应用设置"（→ config）。 即使用户没说"用 MyAgents 做"几个字，只要意图能映射到上述能力之一，就该走这个 skill。 反向边界：纯业务任务（写代码、查资料、读文件）不归这里；用户自己会话里给 AI 排任务用 im-cron MCP，不是这里。
task-alignment
by hAcKlycAlignment conversation starting from a 想法/idea. Co-decides with the user whether the idea should be acted on directly in the current session, or fixed into a formal Task for independent dispatch (one-off or recurring). Handles lightweight 'do it now while we talk', heavyweight 'define precisely, run later or on a schedule', and 'just help me think about this' — all on the same skill. Use when the user arrives via the 想法 panel's 'AI 讨论' button (parameter dictionary in the first message), or says 'let's think this through', 'help me plan this', 'I want to explore X', 'I have an idea', '/task-alignment'. Also use proactively when a user jumps into a complex task without defining scope or success criteria — pause, align, and help them pick the right vessel (this session vs. a task).
task-implement
by hAcKlycAutonomous task execution driven by task documents from /task-alignment (living under `~/.myagents/tasks/<taskId>/`). Reads task.md as the goal, decomposes work, delegates to subagents when appropriate, runs independent verification, and delivers results. Acts as a UserProxy Agent — the human's representative during autonomous execution. Use when the user dispatches a task from the 任务 panel or explicitly wants to start execution. Trigger phrases include '/task-implement', 'start the task', 'go ahead and implement', 'execute the plan', or when the user confirms alignment documents and says something like 'looks good, go'.
prompt-writer
by hAcKlycMethodology for writing or improving prompts and system prompts that drive any LLM. Use when authoring or revising a prompt for a model task — grouping, classification, extraction, generation, copywriting, labeling, agent instructions, prompt templates, skill instructions — to decide how much to constrain the model based on the task type (open-ended vs single-correct-answer) and write the most fitting instructions. Triggers: "write a prompt", "help me write or improve a prompt", "how should I change this prompt", "this prompt isn't working", "write instructions for the model", "prompt-writer". Not for: answering the user's question directly, or writing articles and documents meant for human readers (those are not prompts that drive a model).
hatch-pet
by hAcKlycCreate, repair, validate, visually QA, and package MyAgents/Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker desktop pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.
support
by hAcKlycMyAgents 用户问题响应与客服支持工作流。**任何时候用户在描述困难、报错、异常、不工作的情况—— 以及前端"召唤小助理"入口主动注入的诊断请求——都触发此 skill**。覆盖：(1) 功能异常 / 报错 / 崩溃的根因诊断， (2) 配置错误导致功能失效的排查与修复（修复时配合 `/myagents-cli` skill），(3) 功能使用困惑的解答， (4) 产品建议与功能需求收集。核心准则：**问题语境下「先理解后行动」压 CLAUDE.md 的「行动优先」**—— 先用 boot banner + CLI 只读命令 + 日志取证搞清根因，再决定是直接修、解释、提 Bug 还是提 Feature。 配置错和使用困惑要直接解决，不轻易升级到 Issue 提交。
agent-browser
by hAcKlycBrowser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
docx
by hAcKlycComprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
download-anything
by hAcKlycFind and download virtually any digital resource from the internet — ebooks, academic papers, movies, TV shows, music, software, images, fonts, courses, and more. Covers both English and Chinese internet ecosystems. Includes CLI tool workflows (yt-dlp, aria2, gallery-dl, spotdl), resource site directories, cloud drive search engines (百度/阿里/夸克网盘搜索), and search techniques (Google dorks). Use when the user wants to: (1) download a video, audio, or media from a URL, (2) find and download an ebook or academic paper, (3) find and download software, (4) search for any digital resource, (5) batch download images or media from a gallery/site, (6) download torrents or magnet links, (7) find free stock assets (images, video, audio, fonts), (8) search Chinese cloud drives for resources, or (9) any task involving finding or downloading digital content from the internet.
pptx
by hAcKlycPresentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks
download-anything
by hAcKlycFind and download virtually any digital resource from the internet — ebooks, academic papers, movies, TV shows, music, software, images, fonts, courses, and more. Covers both English and Chinese internet ecosystems. Includes CLI tool workflows (yt-dlp, aria2, gallery-dl, spotdl), resource site directories, cloud drive search engines (百度/阿里/夸克网盘搜索), and search techniques (Google dorks). Use when the user wants to: (1) download a video, audio, or media from a URL, (2) find and download an ebook or academic paper, (3) find and download software, (4) search for any digital resource, (5) batch download images or media from a gallery/site, (6) download torrents or magnet links, (7) find free stock assets (images, video, audio, fonts), (8) search Chinese cloud drives for resources, or (9) any task involving finding or downloading digital content from the internet.
task-alignment
by hAcKlycAlignment conversation starting from a user's rough idea. Co-decides with the user whether the idea should be acted on directly in the current session, or fixed into a formal Task by producing four documents in `.task/` for independent dispatch. Handles lightweight 'do it now while we talk', heavyweight 'define precisely, run later', and 'just help me think about this' — all on the same skill. Use when the user describes a task or intent they want to align on. Trigger phrases include 'help me plan this', 'let's think this through', 'I want to explore X', 'I have an idea', '/task-alignment'. Also use proactively when a user jumps into a complex task without defining scope or success criteria — pause, align, and help them pick the right vessel (this session vs. a task).
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.