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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basedagents
by LeoYeAISearch, scan, and interact with the BasedAgents.ai agent registry — the public identity and reputation layer for AI agents. Look up agents, check reputation scores, scan npm/GitHub/PyPI packages for security issues, probe MCP endpoints, browse tasks, and send agent-to-agent messages.
backstage
by LeoYeAIAnti-drift protocol script. Ensures parity between docs and system. Triggers: 'bom dia PROJECT' / 'good morning PROJECT' (load project context with health checks)
bettafish-opinion-analysis
by LeoYeAIBettaFish（微舆）多智能体舆情分析系统 - 基于 QueryAgent、MediaAgent、InsightAgent 三引擎并行架构，通过 ForumEngine 实现 Agent 间协作讨论，生成 Word/PDF + 高设计质量 HTML 双格式报告。 当用户需要以下分析时触发此 skill： - 分析某品牌/企业/产品的社交媒体声誉和口碑 - 追踪热点舆情事件（如某车企被抨击、某明星争议事件） - 挖掘特定社媒账号的内容和影响力数据 - 监测竞品舆情动态，进行多品牌对比 - 分析公众对某话题的情绪倾向和态度 - 生成舆情监测报告或危机预警分析 - 需要Word文档/PDF格式的正式报告 - 需要高设计质量的交互式HTML可视化报告 - 需要基于真实数据的深度舆情分析 此 skill 采用 QueryAgent + MediaAgent + InsightAgent 并行架构，通过 ForumEngine 实现 Agent 间讨论协作，执行 3 轮反思循环优化分析结果，最终输出: 1. **Word/PDF 文档** - 使用 docx/pdf subskill 生成，适合正式汇报、打印、存档 2. **高设计质量 HTML 报告** - 使用 frontend-design subskill，独特的编辑杂志风格，交互式可视化 **不使用任何数据库和模拟数据**，所有数据通过 WebSearch/WebFetch/Browser/Curl 实时获取。 即使遇到复杂的多步骤分析需求、需要整合多个数据源、或生成专业格式的舆情报告，也请务必使用此 skill。
bilibili-cc-to-notion
by LeoYeAI将B站视频字幕转换为带截图的Notion学习笔记。 当用户需要从B站视频提取字幕、分析内容并创建Notion学习笔记时，必须使用此技能。 支持BV号、完整URL输入，自动下载CC字幕，智能处理内容，生成带截图标记的结构化学习笔记。 适用于学习、研究、知识整理等场景。
bot-arcade
by LeoYeAIUniversal entertainment and gaming engine for AI agents. Turns any bot into a full arcade — emoji slots, trivia, word games, riddles, dice, fortune drops, scratch cards, boss raids, tournaments, and prediction arenas. Zero external APIs. Zero cost. Pure engagement. Use when: user wants to play games, have fun, be entertained, run group games, host tournaments, check leaderboards, or any entertainment request. Also activates on boredom cues, celebration moments, or competitive banter in group chats.
beekeeping-basics
by LeoYeAIIntroduction to backyard beekeeping including equipment, hive management, and honey harvesting. Use when someone wants to start beekeeping, improve garden pollination, produce honey, or is evaluating whether beekeeping fits their situation.
bizcard
by LeoYeAIBusiness card scanner + Google Contacts manager. Auto-detects business card images, extracts contact info via OCR (imageModel), confirms with user, saves to Google Contacts with configurable name format and card photo. Trigger: image that looks like a business card, or keywords "명함", "bizcard", "연락처 저장". Settings: /bizcard config
bona-movie-production
by LeoYeAIBona Movie Production is Bona Group's film-grade production skill. It covers image generation, image editing, and video generation, using Nano Banana 2 and Nano Banana Pro for images, and Seedance plus generate_video_kling_v3 for videos.
boundaries-saying-no
by LeoYeAISetting and maintaining personal boundaries across all relationships. Use when someone can't say no, feels constantly drained by others' demands, is being guilt-tripped, or needs to establish limits with family, work, friends, or partners.
brand-commercial-os
by LeoYeAI品牌商务谈判全链路自动化系统，生成品牌能力包、GEO知识包、跨平台内容包、报价策略包、分发编排包与谈判总包；适用于平台合作、渠道招商、联名共建等商务场景
btc-signals-pro
by LeoYeAIReal-time Bitcoin trading intelligence API providing market data, AI trade signals, derivatives flow, liquidation heatmaps, live crypto news, economic calendar, historical OHLCV, and 50+ data sources for AI-driven trade decisions and automated trading bots.
byted-emr-skills
by LeoYeAIbyted-emr-skills提供管理火山引擎EMR（火山引擎 E-MapReduce（简称“EMR”）是开源Hadoop生态的企业级大数据分析系统，完全兼容开源）的技能，包括管理EMR on ECS集群、EMR on VKE集群、EMR serverless队列、计算组、作业模板/实例、日志、监控并提供 EMR Agent 智能诊断与知识问答能力。当用户提及“EMR on ECS集群”、“EMR on VKE集群”、“Serverless 队列”、“Serverless 作业”、“SparkSQL/PrestoSQL/Ray/PySpark/SparkJar 作业”、“作业日志”、“作业监控”、“作业诊断”等需求时，应优先使用此技能。
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.