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.
Querying local SQLite index...
fire-smoke-detection-analysis
by smyx-sunjinhuiDetects fire and smoke in video scenes. Supports both video stream and image analysis. Suitable for fire early warning scenarios such as security surveillance, forest fire prevention, and industrial parks. | 烟火检测技能，对视频场景中火情和烟雾进行检测，支持视频流和图片检测，适用于安防监控、森林防火、工业园区等火灾预警场景
virtual-fence-intrusion-warning-analysis
by smyx-sunjinhuiCustomizes safety zones, identifies babies crawling out or approaching dangerous areas such as bedsides/windowsills, and immediately alerts to protect baby safety. | 虚拟围栏越界预警技能，自定义安全区域，识别婴儿爬出、靠近床边/窗台危险区域立即报警，守护宝宝安全
smyx-plant-wilting-quantification-analysis
by smyx-sunjinhuiAI-powered plant wilting quantification from full-plant images via smart pots or fixed cameras. Detects leaf-stem angle (leaf droop), stem straightness, and leaf turgidity to quantify wilting severity (0-100%). Optionally fuses soil-moisture sensor data to discriminate dehydration (underwatering) vs. waterlogging (root hypoxia), and auto-triggers watering or drainage prompts for precision irrigation. Scenarios: smart pots, home gardening, greenhouses, plant factories. | 通过智能花盆或固定摄像头拍摄植物整体图像，利用AI视觉分析技术检测叶片与茎秆的夹角（叶片下垂角度）、茎秆挺直程度以及叶片舒展度，量化萎蔫程度（0-100%）。可选结合土壤湿度传感器数据，综合判断萎蔫原因是缺水还是水涝（根部缺氧导致）。可自动触发灌溉或排水提示，帮助用户精准浇水。应用场景：智能花盆、家庭园艺、温室大棚、植物工厂。
smyx-pet-drying-box-heat-stress-analysis
by smyx-sunjinhuiTriggers when a user provides a pet drying box area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for pet heat stress signal detection, analyzing open-mouth panting intensity, tongue color (pink/cyanotic), and body movement frequency to identify early heat stress signals, outputting risk levels and supporting auto-cooling or stopping drying. Application scenarios: pet drying boxes, pet grooming stores, pet hospitals. Development reason: prevent heatstroke and improve safety. | 当用户提供宠物烘干箱区域的视频URL或文件时，触发本技能进行烘干箱内热应激预警分析；支持通过上传本地视频或网络视频URL，调用服务端API进行热应激信号识别，分析张口喘气强度、舌体颜色（粉红/紫绀）、身体移动频率，识别热应激早期信号，输出风险等级，支持自动降温或停止烘干。应用场景：宠物烘干箱、宠物美容店、宠物医院。
tcm-constitution-recognition-analysis
by smyx-sunjinhuiDetermines nine TCM constitution types including Yin deficiency, Yang deficiency, Qi deficiency, phlegm-dampness, and blood stasis through facial features and physical signs, and provides personalized health preservation and conditioning suggestions. | 中医体质识别分析技能，通过面部特征与体征判别阴虚、阳虚、气虚、痰湿、血瘀等九种中医体质类型，给出个性化养生调理建议
smyx-elderly-hand-tremor-detection-analysis
by smyx-sunjinhuiUsing a fixed home camera to record video of an elderly person's hand at rest (placed on a table or armrest with no voluntary movement), AI video-motion analysis detects periodic shaking, extracts tremor frequency (Hz) and amplitude (pixel displacement), and identifies the presence of resting tremor (commonly associated with Parkinson's disease and other neurological conditions). | 通过家庭固定摄像头拍摄老年人手部（置于桌面或自然静止）的视频，利用AI视频分析技术检测手部在静止状态下的周期性抖动频率（Hz）和幅度（像素位移），识别是否存在静止性震颤（常见于帕金森病等神经系统疾病）。该技能可作为早期筛查工具，提示家属或护理人员关注老年人神经系统健康，及时就医。
unaccompanied-monitoring-analysis
by smyx-sunjinhuiDetermines when elderly people living alone have no interaction or visitors for extended periods, and actively pushes care reminders to family members, suitable for remote care scenarios for elderly people living alone at home. | 无人陪伴监测技能，判定独居老人长时间无人互动来访，主动推送关怀提醒给家属，适用于居家独居老人远程关怀场景
smyx-plant-leaf-disease-identification-analysis
by smyx-sunjinhuiAI-powered plant leaf disease identification from high-resolution leaf images. Detects disease lesion features (color, shape, distribution, surface deposits) such as white powdery patches (powdery mildew), rust-colored spore pustules (rust), brown necrotic spots (leaf spot), and outputs the most likely disease type with confidence score. Helps users quickly diagnose plant diseases and take timely measures. Scenarios: plant factories, greenhouses, home gardening, farm inspection. | 通过拍摄植物叶片的高清图像，利用AI视觉分析技术识别叶片上的病斑特征（颜色、形状、分布），检测是否有白色粉状物（白粉病）、锈色孢子堆（锈病）、褐色坏死斑（叶斑病）等典型症状，输出最可能的病害类型及置信度。帮助用户快速诊断植物病害，采取防治措施。应用场景：植物工厂、温室大棚、家庭盆栽、园艺养护。
smyx-elderly-long-term-immobility-analysis
by smyx-sunjinhui"Using fixed cameras in multiple zones of a solo-living elder's home (living room, bedroom, kitchen, bathroom, etc.), the system continuously analyzes the video streams to detect human activity (movement, limb actions, gestures, etc.). If no activity is detected within a configured time window (default 12 hours), the system outputs a 'long-term no activity' alert and can notify emergency contacts via app or phone. | 通过独居老人家中的多个区域（客厅、卧室、厨房、卫生间等）固定摄像头，连续分析视频流，检测人体活动（包括移动、肢体动作、手势等）。若在设定的时间窗口内（默认12小时）未检测到任何活动，则输出'长期无活动'预警，并可通过APP或电话通知紧急联系人。"
smyx-egg-incubation-monitoring-analysis
by smyx-sunjinhuiThrough a fixed camera (macro or high-resolution) in the incubator, the system periodically captures surface images of turtle or snake eggs and uses AI visual analysis to detect changes in eggshell colour (normally white or pale yellow; after fertilisation, grey spots or a vascular network may appear), blood streaks (early vascular formation in fertilised eggs, appearing as fine red lines), and embryo silhouette (a dark mass. | 通过孵化箱内的固定摄像头（微距或高分辨率），定期拍摄龟蛋或蛇蛋的表面图像，利用 AI 视觉分析技术检测蛋壳颜色变化（正常为白色或淡黄色，受精发育后可能出现灰斑、血管网络）、血丝（受精卵早期血管形成，呈红色细线状）以及胚胎轮廓（后期可见黑影）。系统每日或每两日自动拍照分析，生成孵化报告。
smyx-fish-egg-incubation-stage-analysis
by smyx-sunjinhuiThrough breeding-tank fixed cameras (macro lens), the system periodically captures high-definition images of fish eggs and uses AI vision analysis to detect egg color changes (transparent → white / black) and embryonic eye-spots (small black dots), identifying incubation stages (unfertilized / early / mid / late-eyespot / hatching). | 通过繁殖缸固定摄像头（微距镜头），定期拍摄鱼卵的高清图像，利用 AI 视觉分析技术检测鱼卵颜色变化（透明 → 发白/发黑）以及胚胎眼睛点（黑色小点）的出现，识别鱼卵的孵化阶段（未受精/早期/中期/晚期/破壳）。系统定时（如每 6 小时）自动分析，输出孵化阶段及建议（如'已出现眼睛点，预计 24 小时内孵化，准备丰年虾'）。
smyx-frog-skin-moisture-assessment-analysis
by smyx-sunjinhuiThrough fixed cameras in rainforest tanks or vivariums, the system captures high-definition images of the dorsal or lateral skin of frogs (such as tree frogs, horned frogs, dart frogs), and uses AI visual analysis to detect skin glossiness (specular reflection intensity) and assess skin moisture levels. | 通过雨林缸或饲养箱固定摄像头，拍摄蛙类（如树蛙、角蛙、箭毒蛙）的背部或侧身皮肤高清图像，利用 AI 视觉分析技术检测皮肤的光泽度（反光强度），评估皮肤的湿润程度。健康的蛙类皮肤应湿润、有光泽；当皮肤干燥时，光泽度显著下降，甚至出现皱褶或白膜。
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.