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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xlsx-skill
by OpenLoafExcel / CSV（.xlsx / .xlsm / .csv / .tsv）读/写/统计/重算/图表一体化。触发场景：阅读单元格、汇总分组、谁最高谁最低、总收入、改单元格或公式、加行列、加合计行、插入图表（bar/line/pie）、冻结首行、加筛选、条件格式、CSV ↔ XLSX 互转、新建花名册/销售表/财务模型、排查 #REF!/#DIV/0! 公式错误、触发重算。典型说法："这表里谁销量最高"、"Q1 总收入"、"把 B2 改成 500"、"加一行合计"、"新建花名册"、"csv 转 xlsx"、"冻结首行"、"A 列负数标红"。用户提到 .xlsx / .csv 文件或最终产出是表格都加载本技能。
project-ops-skill
by OpenLoaf当用户要对 OpenLoaf 的"项目"这一实体做创建、打开、切换、移动、删除、改名时触发。典型说法"新建项目"、"把 ~/code/foo 加进来"、"列出所有项目"。**不用于**：项目内文件读写（→直接用 Read/Edit/Write）、讨论"项目规划 / 需求文档"（→直接回答）、纯 Git 日常操作（→`Bash`）。
schedule-ops-skill
by OpenLoaf当用户要把任务交给后台异步执行、按 cron / 周期性重复、委派给项目 Agent 长时间跑，或查看/审批/取消看板任务时触发。典型说法"每天 9 点帮我 X"、"让 coder agent 跑这个"、"有哪些待审批"。**不用于**：一次性即时答复（→直接用工具）、真实日历会议 / 约会（→calendar-ops-skill）、一次性计划审批（→`SubmitPlan`）。
agent-orchestration-skill
by OpenLoafTriggers when the master Agent faces a multi-step complex task and is deciding whether / how to outsource sub-tasks to built-in subagents (browser / doc-editor / data-analyst / extractor / canvas-designer / coder, etc.). **Not for**: single-step Q&A / reads / simple side effects (master Agent handles directly), or scenarios where `Agent` is already confirmed and `subagent_type` is known.
pptx-skill
by OpenLoaf当用户要求对 PowerPoint 幻灯片（.pptx）做任何操作时触发：阅读、总结、提取、编辑、创建、格式转换均适用。核心认知：PPTX 是视觉结构化文件——图表、排版、示意图等内容靠纯文字抽取无法可靠还原。理解幻灯片内容的正确路径是：用 PptxInspect render 把目标页渲染成 PNG，再交给视觉模型（CloudImageUnderstand）理解，而不是仅靠 text/outline 文字提取。文字工具仅作补充上下文使用。典型说法："总结这份 PPT"、"这个 deck 在讲什么"、"第 3 页讲什么"、"描述第 5 页内容"、"帮我做一份 Q4 汇报 PPT"、"改第 3 页标题"。用户提到 .pptx 文件、deck、幻灯片、演示文稿时都加载本技能。
calendar-ops-skill
by OpenLoafTriggers when the user wants to query or modify real calendar data: checking today's or this week's agenda, spotting conflicts, creating meetings, rescheduling, or deleting events. Typical phrasings include "what time is my meeting tomorrow", "book xx for me", or "schedule a meeting". **Do not use for**: casually mentioning times or countdowns in conversation (→ answer directly), or recurring background jobs like "run this script at 9am every day" (→ schedule-ops-skill).
pptx-skill
by OpenLoafTriggers whenever the user asks to do anything with PowerPoint slides (.pptx): read, summarize, extract, edit, create, or convert. Key insight: PPTX files are visually structured — charts, diagrams, and layouts cannot be reliably captured by text extraction alone. The correct approach for understanding slide content is to render the target slide(s) as PNG images (via PptxInspect render) and pass them to a vision model (CloudImageUnderstand), not to rely solely on text/outline extraction. Use text tools (outline / text / shapes) only as supplementary context. Typical phrasings: "summarize this PPT", "what is this deck about", "what's on slide 3", "describe page 5", "make me a Q4 report deck", "change the title of slide 3". Load this skill whenever the user mentions a .pptx file, deck, slides, or presentation.
schedule-ops-skill
by OpenLoafTrigger when the user wants to hand a task to background async execution, run it on a cron/recurring schedule, delegate it to a project Agent for a long run, or inspect/approve/cancel tasks on the board. Typical phrasings: "every day at 9am help me X", "let the coder agent run this", "what's pending approval". **Not for**: one-shot immediate answers (→ just use tools directly), real calendar meetings/appointments (→ calendar-ops-skill), one-off plan approvals (→ `SubmitPlan`).
xlsx-skill
by OpenLoafUnified read / write / stats / recalc / chart capability for Excel / CSV (.xlsx / .xlsm / .csv / .tsv). Trigger scenarios: reading cells, summarizing, grouping, highest/lowest values, total revenue, editing cell values or formulas, inserting rows/columns, adding total rows, inserting charts (bar/line/pie), freezing panes, auto-filter, conditional formatting, CSV ↔ XLSX conversion, building rosters / sales sheets / financial models, diagnosing #REF! / #DIV/0! formula errors, triggering recalculation. Typical phrasings: "who has the highest sales in this sheet", "total Q1 revenue", "change B2 to 500", "add a total row", "create a new roster", "convert csv to xlsx", "freeze the first row", "highlight negatives in column A red". Load this skill whenever the user mentions an .xlsx / .csv file or the final deliverable is a spreadsheet.
calendar-ops-skill
by OpenLoaf当用户要对真实日历数据做查询或增删改时触发：看今天/本周安排、查冲突、建会议、改时间、删日程。典型说法"我明天几点开会"、"帮我约 xx"、"schedule a meeting"。**不用于**：对话里口头提到时间或倒计时（→直接回答）、周期性后台任务如"每天 9 点跑脚本"（→schedule-ops-skill）。
docx-skill
by OpenLoafWord 文档（.docx）读/写/转/评审一体化。触发场景：总结 docx、读段落/大纲/表格/图片/评论/修订、替换正文、插入图片、改页面设置、重建目录、加评论或回复、加修订（insert/delete/replace）、接受/驳回修订、docx ↔ pdf/html/md/txt 互转。典型说法："帮我总结这份 Word"、"第二段改成 XXX"、"把全部'A 公司'换成'B 公司'"、"加一条修订批注"、"接受所有修订"、"docx 转 pdf"、"从这些要点生成销售报告"。用户提到 .docx / .doc 文件或以 Word 文档为产出目标都加载本技能。
settings-guide-skill
by OpenLoafTriggers when the user asks about settings, preferences, API keys, model switching, theme / language / shortcuts / proxy / backup, or related failures (key invalid, model not found) within the OpenLoaf desktop product — where to configure and how to configure them. **Not for**: code configuration files inside a project such as tsconfig / .env (→ use Read/Edit directly), general programming questions (→ answer directly), or system-level OS settings.
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.