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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pg-data
by tyrchenGenerate safe, read-only PostgreSQL queries from natural language. Use when users need to query blog_small, ecommerce_medium, or saas_crm_large databases. Supports query generation, execution, and result analysis with confidence scoring.
site-slides
by tyrchenGenerate presentation slides from images or PDF files. Use when user wants to create slides, generate presentations, or convert PDF to slides for the training camp website. Triggers on keywords like "slides", "presentation", "幻灯片", "演示文稿".
gemini-image
by tyrchenReference guide for using google-genai Python library to generate images with gemini-3-pro-image-preview model. Use this skill when building new projects that need Gemini image generation capabilities, to understand the correct API patterns, configuration options, and best practices.
impl
by tyrchenImplement one phase of ./specs/91-impl-plan.md end-to-end with high quality bars (correctness, elegance, performance), then run an independent code review against the relevant specs and fix every valid finding before declaring done. Use whenever the user says "build phase N", "implement the next phase", "land M0/M1/M2/M3", "follow the impl plan", "ship phase X entirely", "based on @specs/91-impl-plan.md think ultra hard and build phase X", or asks for a phase-shaped slice of the spec set. Trigger even when the user does not say "impl" if they reference an impl plan / roadmap milestone and ask Claude to build it.
research
by tyrchenVendor reference repos as git submodules under ./vendors and produce deep research memos under ./docs/research covering architecture, design, key data structures, and load-bearing algorithms. Use whenever the user says "do research on X", "study how Y works", "submodule this repo and look into it", "understand the design of Z before we start", "spike on …", references prior-art crates / repos that should be evaluated, or asks to refer to ./vendors before designing or implementing. Trigger even when the user does not say the word "research" if they paste GitHub URLs and ask Codex to learn from them, compare alternatives, or extract patterns.
spec
by tyrchenTurn a feature idea or rough requirement into a complete, dependency-ordered spec set under ./specs — PRD, component designs, glossary, security/perf/test cross-cuts, key-decisions log, stakeholder roadmap, and engineer-facing implementation plan — cross-referenced with prior-art memos in ./docs/research and vendored code in ./vendors. Use whenever the user says "write the spec", "design this", "let's plan X", "produce a PRD / impl plan / roadmap", "restructure the specs", "review and re-organise the design", "think ultra hard and split this into phases", or describes a non-trivial system that needs a written design before code. Trigger even when the user does not say "spec" if they ask for phased delivery, milestone exit criteria, or a build-order graph.
impl
by tyrchenImplement one phase of ./specs/91-impl-plan.md end-to-end with high quality bars (correctness, elegance, performance), then run an independent code review against the relevant specs and fix every valid finding before declaring done. Use whenever the user says "build phase N", "implement the next phase", "land M0/M1/M2/M3", "follow the impl plan", "ship phase X entirely", "based on @specs/91-impl-plan.md think ultra hard and build phase X", or asks for a phase-shaped slice of the spec set. Trigger even when the user does not say "impl" if they reference an impl plan / roadmap milestone and ask Codex to build it.
spec
by tyrchenTurn a feature idea or rough requirement into a complete, dependency-ordered spec set under ./specs — PRD, component designs, glossary, security/perf/test cross-cuts, key-decisions log, stakeholder roadmap, and engineer-facing implementation plan — cross-referenced with prior-art memos in ./docs/research and vendored code in ./vendors. Use whenever the user says "write the spec", "design this", "let's plan X", "produce a PRD / impl plan / roadmap", "restructure the specs", "review and re-organise the design", "think ultra hard and split this into phases", or describes a non-trivial system that needs a written design before code. Trigger even when the user does not say "spec" if they ask for phased delivery, milestone exit criteria, or a build-order graph.
ai-image
by tyrchenGenerate AI images using OpenAI's gpt-image-1 model with customizable aspect ratios and artistic themes. Use when the user wants to create images, generate artwork, or mentions image generation with specific styles like Ghibli, futuristic, Pixar, oil painting, or Chinese painting.
council
by tyrchenCouncil · 智囊团：蒸馏真人思维框架为Advisor，并支持多Advisor圆桌讨论。 所有Advisor以persona文件形式存储在 personas/ 目录下，由Council统一管理。 三种用法： (A) 蒸馏：输入人名/主题/模糊需求 → 深度调研 → 提炼心智模型 → 生成Advisor persona (B) 激活：加载已有Advisor，以其视角回答问题 (C) 圆桌：召集多个Advisor → 独立发言 → 交叉质疑 → 综合输出 触发词（蒸馏）：「蒸馏XX」「造skill」「做个XX视角」「XX的思维方式」「更新XX的persona」 触发词（激活）：「用XX的视角」「XX会怎么看」「XX模式」「切换到XX」「ask XX」 触发词（圆桌）：「问问council」「圆桌讨论」「让XX和YY讨论」「ask the council」「council session」 模糊需求也触发：「我想提升决策质量」「有没有一种思维方式能帮我...」「我需要一个思维顾问」
codex-code-review
by tyrchenPerform comprehensive code reviews using OpenAI Codex CLI. This skill should be used when users request code reviews, want to analyze diffs/PRs, need security audits, performance analysis, or want automated code quality feedback. Supports reviewing staged changes, specific files, entire directories, or git diffs.
chat-history
by tyrchenExtract and organize Claude Code session history into project .chats directory. Use when users want to document their Claude sessions, export conversation inputs, or maintain a log of instructions given to Claude.
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.