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...
bestblogs-profile
by ginobefunUse this skill when the user wants to set up, inspect, or update their BestBlogs interest profile and onboarding state (cold start). Triggers include "帮我做 BestBlogs 冷启动 / 设置兴趣标签 / 看我现在的画像 / bestblogs 的 onboarding", "set up my BestBlogs profile", "what topics have I chosen on BestBlogs". Invokes the `bestblogs` CLI.
bestblogs-explain
by ginobefunUse this skill when the user wants to understand the "why" behind BestBlogs recommendations — view their interest profile summary, see which followed sources are driving their daily brief, or inspect the score breakdown of a specific resource. Triggers include "BestBlogs 为什么推这条 / 看我的画像 / 我关注的源哪些最有价值 / 这篇文章评分多少", "why did BestBlogs recommend this", "which of my followed sources contribute most", "show score for this resource". Invokes the `bestblogs` CLI.
bestblogs-discover
by ginobefunUse this skill when the user wants to find today's most worth-reading content from BestBlogs, browse personalized feeds, check trending items, search specific topics, or explore curated newsletters. Triggers include "今天有什么值得读的 / BestBlogs 推荐 / 本周 BestBlogs 热趋 / 搜一下 AI coding / 看关注源的最新 / 最近有什么好周刊", "what's worth reading today", "bestblogs trending this week", "bestblogs discover", "search BestBlogs". Invokes the `bestblogs` CLI.
bestblogs-capture
by ginobefunUse this skill when the user wants to save, annotate, or review their BestBlogs reading — create/list/update/remove bookmarks, add highlights with optional notes, view highlight-only vs. note-only lists, and manage reading history. Triggers include "收藏这篇 / 给这段划线 / 加个笔记 / 看我的收藏 / 我的笔记 / 我的阅读历史 / 删掉这条历史", "bookmark this on BestBlogs", "highlight this quote", "show my notes", "clear my BestBlogs reading history". Invokes the `bestblogs` CLI.
bestblogs-brief
by ginobefunUse this skill when the user asks about today's daily brief, morning digest, or BestBlogs highlights. Triggers include "今天早报 / 今日早报 / BestBlogs 早报 / 今天有啥好文 / 今日精选 / 给我看早报", "today's brief", "today's digest", "BestBlogs morning brief", "today's highlights on BestBlogs", "what's in today's brief". Invokes the `bestblogs` CLI.
bestblogs-trending
by ginobefunUse this skill when the user asks about trending, popular, or hot content on BestBlogs. Triggers include "BestBlogs 本周热门 / 本周流行什么 / 最近热门文章 / 今日热门 / 本月 BestBlogs 精选", "BestBlogs trending", "what's trending on BestBlogs", "popular this week", "hot articles this month", "trending tech content". Invokes the `bestblogs` CLI.
bestblogs-topic
by ginobefunUse this skill when the user asks about a specific topic, subject area, or wants to explore curated topic pages on BestBlogs. Triggers include "BestBlogs 有什么主题 / 查一下 AI 编程主题 / 给我看 Claude 主题页 / 大模型相关主题", "BestBlogs topic list", "show me topics about AI", "what topics does BestBlogs have", "topic details for vibe-coding", "browse topic pages". Invokes the `bestblogs` CLI.
bestblogs-read
by ginobefunUse this skill when the user wants to deep-read a specific BestBlogs resource (article / podcast / video / tweet) — fetch its metadata + markdown so a local LLM or reader can consume it, and automatically report the read behavior back to BestBlogs. Triggers include "深度阅读这篇 / 拉这篇内容的 markdown / bestblogs read / 给我看看 RAW_xxx 的正文", "deep read this BestBlogs article", "pull markdown for resource RAW_...". Invokes the `bestblogs` CLI.
deep-reading-analyst
by ginobefunComprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for analysis.
smart-content-creator
by ginobefunTransform reading notes and insights into polished, authentic content (blogs, social media, visualizations) that preserves your unique voice and avoids AI-style writing. Creates content that sounds unmistakably human.
article-recommender
by ginobefunGenerate three-version article recommendations (standard, concise, and personal commentary) in both Chinese and English for BestBlogs.dev weekly newsletter. Use when users request article recommendations，推荐语，推荐理由，or ask to write recommendations for newsletter content. Triggered by phrases like "帮我编写推荐理由", "生成推荐语", "write a recommendation", or when presenting curated content.
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.