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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discounted-cashflow-model
by j0x7c4Builds a discounted cash flow (DCF) valuation model, calculating enterprise value, equity value, and per-share price with a growth-rate × discount-rate sensitivity analysis matrix. Triggered by requests like 'do a DCF on [company]', 'run a valuation', 'calculate WACC/discount rate', or mentions of free cash flow, terminal value, or sensitivity analysis.
sun-path
by j0x7c4Analyzes sunlight and shadows for architectural design, generating sun path diagrams, solar position data, shadow plots, and thermal comfort charts. Triggered by requests about sun position, shadow analysis, annual sun hours, or diagrams for a specific location and time.
sunlight-analysis
by j0x7c4生成太阳轨迹图，计算实时太阳位置，分析建筑阴影投射范围与全年日照时数，并提供热舒适度评估，适用于建筑设计与场地环境分析。当用户询问太阳位置、请求绘制太阳轨迹、分析建筑阴影、计算全年日照时间、评估热舒适度或进行地形阴影分析时触发。
sci-paper
by j0x7c4Structured guidance for composing, formatting, and presenting scientific research papers for top-tier venues (CVPR, ICCV, NeurIPS, ICML, ACL, ICLR, etc.). Covers the full workflow: section-by-section drafting, figure and table design, LaTeX typography, narrative logic, and submission-ready polishing. Use to write, revise, or format a manuscript into camera-ready or preprint PDF.
fashion-sketch-cn
by j0x7c4创建专业服装技术规格包（Tech Pack），包含系列概览、款式规格、工艺细节、尺寸表、面料库、物料清单（BOM）、质量标准与签核页。适用于服装、鞋履、配饰等需要结构化、专业技术文档格式的品类。
fashion-sketch
by j0x7c4Create professional apparel technical specification packages (tech packs) with collection overviews, style specs, construction details, measurement charts, fabric libraries, bills of materials, quality standards, and sign-off pages. Use for garments, footwear, or accessories requiring a structured, professional technical document format.
equity-research-report
by j0x7c4Create institutional-grade investment research reports with sell-side styling, dense information layout, and comprehensive section structure. Use for equity, fixed-income, strategy, sector, derivatives, ETF, or quantitative research. Mimics Goldman Sachs, Morgan Stanley, or JPMorgan style with cover pages, exhibit-numbered charts, glossary, and disclosures. Supports PDF, DOCX, and PPTX output.
adhd-daily-planner
by j0x7c4ADHD 生活管理助手，通过每日规划、任务拆解、时间感知辅助、优先级排序、虚拟陪伴工作和情绪支持来帮助管理日常。当用户寻求帮助规划日程、克服任务瘫痪、应对拖延，或提到时间盲、难以启动任务、注意力分散等 ADHD 相关挑战时触发此技能。
cross-platform-adapter
by j0x7c4Adapts and repurposes content (blog posts, articles, reports, newsletters) for LinkedIn, Twitter/X, WeChat Official Accounts, Zhihu, and Slack — adjusting tone, length, formatting, and language to match each platform's norms, including English-Chinese cross-language adaptation. Use when a user wants to repurpose, cross-post, adapt, or convert existing content for different platforms.
design-system-builder
by j0x7c4Extract design systems from reference UI images and generate implementation-ready UI design prompts. Use when users provide UI screenshots/mockups and want to create consistent designs, generate design systems, or build MVP UIs matching reference aesthetics.
dev-guide-generator
by j0x7c4Generates complete technical tutorials from prerequisites and environment setup to core steps, troubleshooting, and a final cheatsheet. Trigger on requests to write a tutorial, create a setup guide, organize steps for beginners, or keywords like step-by-step, quickstart, or how-to guide.
email-to-calendar
by j0x7c4Extract calendar events and deadlines from emails and create calendar entries. Two modes: monitor your inbox directly, or process emails you forward to a dedicated address. Trigger when a user asks to scan emails for events, mentions an event in an email, or wants to add something from an email to their calendar.
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.