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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eldercare-daily-report
by nclamvnBáo cáo tình trạng bà mỗi ngày lúc 21:00. Tổng hợp dữ liệu từ tất cả eldercare skills: hoạt động, cuộc gọi, cảnh báo, môi trường phòng. Viết tự nhiên bằng tiếng Việt, gửi Zalo group gia đình. Data sources: eldercare-monitor, eldercare-sos, eldercare-videocall, eldercare-companion, Home Assistant sensors.
eldercare-sos
by nclamvnHệ thống SOS khẩn cấp cho bà nội. Nhận trigger từ: nút vật lý Zigbee (qua Home Assistant), AI detection (từ eldercare-monitor), hoặc gia đình gõ "SOS" trong Zalo/Telegram. Escalation chain tự động: Level 1 (Zalo) → Level 2 (Phone) → Level 3 (gọi tất cả). Gửi kèm camera snapshot. Có cancel mechanism.
eldercare-companion
by nclamvnBạn đồng hành AI cho bà nội. Gồm 4 chức năng: A) Phát nhạc xưa (bolero, cải lương) qua loa phòng bà B) Đọc truyện bằng TTS tiếng Việt, nhớ vị trí đọc dở C) Nhắc sinh hoạt mỗi 2 giờ (uống nước, đổi tư thế, ăn nhẹ) D) Nhận voice command đơn giản từ bà (giọng yếu, fuzzy match) Mọi audio output đều VOLUME CAO (bà nặng tai). TTS tốc độ chậm hơn bình thường (rate 0.8).
eldercare-exercise
by nclamvnHướng dẫn bài tập nhẹ cho người cao tuổi nằm giường hoặc ngồi. TTS đọc từng bước chậm rãi, đếm giữ, nghỉ giữa các bài. Trigger: Gia đình nhắn "tập thể dục cho bà" hoặc cron hàng ngày. Tất cả bài tập AN TOÀN cho người 90+ nằm giường. Disabled by default — gia đình bật khi sẵn sàng.
eldercare-emergency-contacts
by nclamvnDanh sách liên lạc khẩn cấp cho người cao tuổi Việt Nam. Tích hợp vào SOS Level 3: cung cấp thông tin y tế cho cấp cứu. Gia đình chat "cấp cứu" hoặc "emergency" để xem danh sách. Lưu medical profile để sẵn sàng cho nhân viên cấp cứu.
eldercare-health-log
by nclamvnTheo dõi sức khoẻ người thân cao tuổi. Gia đình nhập qua chat: - Huyết áp: "huyết áp bà 130/80" hoặc "HA 130/80" - Đường huyết: "đường huyết 120" hoặc "ĐH 120" - Nhịp tim: "nhịp tim 75" hoặc "NT 75" - Cân nặng: "cân nặng 45kg" hoặc "CN 45" - Nhiệt độ cơ thể: "nhiệt độ 37.2" hoặc "sốt 38.5" - SpO2: "SpO2 96" hoặc "oxy 96" - Ghi chú tự do: "bà ho nhiều hôm nay", "bà ăn ít" Dữ liệu lưu memory, hiển thị trend trong daily report. Cảnh báo khi chỉ số ngoài ngưỡng an toàn.
eldercare-medication
by nclamvnNhắc uống thuốc cho người cao tuổi. Gia đình cấu hình toa thuốc qua Zalo hoặc config UI. Hệ thống nhắc đúng giờ qua TTS + Zalo. Hỗ trợ nhiều thuốc, nhiều giờ, ghi nhận đã uống / chưa uống. Disabled by default — chỉ bật khi gia đình thêm toa thuốc.
eldercare-weather-alert
by nclamvnCảnh báo thời tiết cực đoan cho gia đình chăm sóc người cao tuổi. Check thời tiết 2 lần/ngày (6h + 18h). Cảnh báo: - Lạnh < 18C → "Đắp thêm chăn cho bà" - Nóng > 35C → "Bật quạt/AC, cho bà uống nước" - Mưa bão → "Đóng cửa sổ phòng bà" - Độ ẩm thấp < 40% → "Bật máy tạo ẩm" Dùng 3 nguồn: HA outdoor sensor, HA indoor sensor, Open-Meteo API.
eldercare-daily-report
by nclamvnBáo cáo tình trạng bà mỗi ngày lúc 21:00. Tổng hợp dữ liệu từ tất cả eldercare skills: hoạt động, cuộc gọi, cảnh báo, môi trường phòng. Viết tự nhiên bằng tiếng Việt, gửi Zalo group gia đình. Data sources: eldercare-monitor, eldercare-sos, eldercare-videocall, eldercare-companion, eldercare-sleep-tracker, eldercare-exercise, eldercare-weather-alert, eldercare-visitor-log, eldercare-multi-room, eldercare-health-log, eldercare-medication, Home Assistant sensors.
eldercare-profiles
by nclamvnQuản lý hồ sơ người thân cao tuổi. Hỗ trợ chăm sóc nhiều người cùng lúc (bà nội, ông nội, bố mẹ già...). Mỗi người có profile riêng với sensors, contacts, config riêng. Gia đình quản lý qua chat: - "thêm ông nội" → tạo profile mới - "danh sách người thân" → list all elders - "xoá profile ông nội" → deactivate (không xoá data) Auto-migration: Nếu chưa có profiles → tự tạo "Bà Nội" từ config hiện tại. Không cần gia đình làm gì.
eldercare-sos
by nclamvnHệ thống SOS khẩn cấp cho bà nội. Nhận trigger từ: nút vật lý Zigbee (qua Home Assistant), AI detection (từ eldercare-monitor), hoặc gia đình gõ "SOS" trong Zalo/Telegram. Escalation chain tự động: Level 1 (Zalo) → Level 2 (Phone) → Level 3 (gọi tất cả). Gửi kèm camera snapshot. Có cancel mechanism.
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.