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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lazyllm-skill
by LazyAGILazyLLM framework for building multi-agent AI applications. Use when task mentioned LazyLLM or AI program for: (1) Flow orchestration - linear, branching, parallel, loop workflows for complex data pipelines, (2) Model fine-tuning and acceleration - finetuning LLMs with LLaMA-Factory/Alpaca-LoRA/Collie and acceleration with vLLM/LMDeploy/LightLLM. Includes comprehensive code examples for all components, (3) RAG systems - knowledge-based QA with document retrieval, vectorization, and generation, (4) Agent development - single/multi-agent systems with tools, memory, planning, and web interfaces.
sciverse-paper-search
by LazyAGIUse this skill for scientific literature search, evidence retrieval, paper metadata screening, and cited research synthesis with Sciverse. This LazyLLM-adapted version supports SciverseSearch search, meta_search, meta_catalog, and get_content only; it does not assume full Sciverse MCP resource or attachment APIs are available.
paper-search
by LazyAGIPrimary skill for searching, retrieving, and reading academic papers from arXiv.
ui-safe-form-patterns
by LazyAGIImplement or refactor UI forms with defense-in-depth validation, safe input handling, reliable submission behavior, and privacy-aware error handling. Use when building login, registration, profile, payment, admin, or file-upload forms.
ui-resilient-accessibility
by LazyAGIBuild and review UI components for robust accessibility and runtime resilience, including semantic structure, keyboard support, loading and error states, retry flows, and predictable async behavior.
systematic-document-and-literature-review
by LazyAGIUse this skill when the user wants a systematic review, thematic synthesis, or cross-document analysis across multiple academic papers OR provided general documents (reports, internal memos, web articles). Retrieves sources via arXiv, internal Knowledge Base (KB), or direct URLs. Analyzes the content within the current context and outputs a fully structured synthesis report directly in the chat.
single-document-review
by LazyAGIUse this skill when the user requests to review, analyze, critique, or summarize a SINGLE academic paper, general document, internal report, proposal, or web article. Supports comprehensive structured reviews covering methodology/logic assessment, strengths, weaknesses, and constructive feedback. Retrieves content using native tools (`url_fetch`, `kb_search`, `arxiv_search`) and outputs the analysis directly in the chat.
deep-research
by LazyAGIUse this skill instead of WebSearch for ANY question requiring comprehensive, multi-source research. Trigger on queries explicitly asking for deep analysis, such as "research X", "deep dive into X", "comprehensive review of X", "systematic comparison between X and Y", "investigate the landscape of X", or Chinese equivalents like "调研一下X", "深入研究X", "全面对比X与Y", "X的详细综述", "深度调查X". Do NOT trigger on simple factual questions. Provides systematic multi-angle research methodology, prioritizing internal knowledge base (KB) searches before performing broad web searches. Use this proactively when the user's question needs extensive information gathering and synthesis.
ui-secure-review
by LazyAGIReview frontend and UI changes for concrete security risks such as XSS, unsafe URL handling, token leakage, missing origin checks, and client-side authorization gaps. Use when users ask for a UI code review focused on safety and reliability.
lazyllm-review
by LazyAGIRun AI code review using the LazyLLM review CLI tool (lazyllm review / lazyllm review-local). Use when the user mentions: review, code review, review-local, review PR, fix review issues, post review to GitHub, lazyllm review, PR review, review 代码, 代码 review, review 问题, 修复 review, fix review comments, fix bug from review, 修复 review 评论, 处理 PR review 意见, 根据 review 修 bug, 回复 review 评论。 Covers installation, model/API-key config, local review JSON processing, GitHub auth, posting review comments to PRs, and fixing bugs from PR review comments.
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.