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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youtube-downloader
by daymadeDownload YouTube videos and HLS streams (m3u8) from platforms like Mux, Vimeo, etc. using yt-dlp and ffmpeg. Use this skill when users request downloading videos, extracting audio, handling protected streams with authentication headers, or troubleshooting download issues like nsig extraction failures, 403 errors, or cookie extraction problems.
llm-icon-finder
by daymadeFinding and accessing AI/LLM model brand icons from lobe-icons library. Use when users need icon URLs, want to download brand logos for AI models/providers/applications (Claude, GPT, Gemini, etc.), or request icons in SVG/PNG/WEBP formats.
llm-wiki-setup
by daymadeCo-create a personal investment-research LLM Wiki (Andrej Karpathy's pattern) where the user's OWN analysis framework becomes a living CLAUDE.md — by interviewing them, NOT by handing them a template. Use whenever the user wants to build a compounding research knowledge base, 投研第二大脑, 投研知识库, or 个人投研 wiki; instantiate Karpathy's LLM Wiki gist for finance/investing; turn their stock-picking, analyst-tracking, or earnings-watching workflow into a structured markdown vault; or build a wiki tracking companies / industries / macro / analysts over time. Pure markdown + wikilinks, NO RAG / vector DB (Karpathy's core idea — do not over-engineer). Also triggers for ingesting research reports / earnings calls / expert notes into an existing wiki, and for post-earnings prediction→fulfillment reviews. Core value = extracting the user's personal investment preferences into THEIR OWN schema, never imposing a standard one.
qa-expert
by daymadeThis skill should be used when establishing comprehensive QA testing processes for any software project. Use when creating test strategies, writing test cases following Google Testing Standards, executing test plans, tracking bugs with P0-P4 classification, calculating quality metrics, or generating progress reports. Includes autonomous execution capability via master prompts and complete documentation templates for third-party QA team handoffs. Implements OWASP security testing and achieves 90% coverage targets.
ui-designer
by daymadeExtract design systems from reference UI images and generate implementation-ready UI design prompts. Use when users provide UI screenshots/mockups and want to create consistent designs, generate design systems, or build MVP UIs matching reference aesthetics.
benchmark-due-diligence
by daymadeRuns adversarial due-diligence on a benchmark the user envies — a founder, KOL, company, or product whose claimed success looks inflated — splitting marketing bubble from real signal, then mapping the validated playbook onto the user's own resources. Use whenever the user wants to 尽调/对标/拆解 a competitor or role-model, 抄/偷师 someone's playbook, suspects 水分/泡沫 in their claims (#1 on Product Hunt, 0-to-1M users, funding, 估值几个亿), asks whether wins are 真本事 vs 运气/时机, or says someone is 太成功了/crushing it and wants the real story — even if they never say 尽调. Prefer over deep-research for debunking inflated claims and extracting a replicable playbook rather than a neutral briefing.
cloudflare-troubleshooting
by daymadeInvestigate and resolve Cloudflare configuration issues using API-driven evidence gathering. Use when troubleshooting ERR_TOO_MANY_REDIRECTS, SSL errors, DNS issues, or any Cloudflare-related problems. Focus on systematic investigation using Cloudflare API to examine actual configuration rather than making assumptions.
competitors-analysis
by daymadeAnalyze competitor repositories with evidence-based approach. Use when tracking competitors, creating competitor profiles, or generating competitive analysis. CRITICAL - all analysis must be based on actual cloned code, never assumptions. Triggers include "analyze competitor", "add competitor", "competitive analysis", or "竞品分析".
transcript-fixer
by daymadeCorrects speech-to-text transcription errors using dictionary rules and AI-powered analysis. Builds personalized correction databases that learn from each fix. Triggers when working with ASR/STT output containing recognition errors, homophones, garbled technical terms, or Chinese/English mixed content. Also triggers on requests to clean up meeting notes, lecture transcripts, interview recordings, or any text produced by speech recognition. Use this skill even when the user just says "fix this transcript" or "clean up these meeting notes" without mentioning ASR specifically.
claude-md-progressive-disclosurer
by daymadeOptimize / slim down / restructure a CLAUDE.md (or AGENTS.md) using progressive disclosure — move low-frequency detail to Level 2 references while keeping Level 1 lean, WITHOUT losing information. Use this whenever the user asks to optimize / 精简 / 瘦身 / 重构 CLAUDE.md, asks "CLAUDE.md 是不是太大了 / 太长了" (is my CLAUDE.md too big / too long / bloated), wants to 把内容拆到 reference / 外部 / Level 2, do 整段外移 / 渐进式披露 / progressive disclosure, or whenever a CLAUDE.md duplicates info across files or the LLM keeps failing to follow its rules. ALSO trigger the moment an optimization turns into moving / cutting / compressing sections of a CLAUDE.md — even mid-task while another claude-md skill is already running. Distinct from claude-md quality auditors / scorers: this is the restructuring-and-offloading methodology that guarantees zero information loss (grep-verified pointers, verbatim moves, 5b content-integrity audit).
pdf-creator
by daymadeConvert markdown files to professional PDF documents with proper Chinese font support, theme system, and visual self-check. Use whenever the user asks to create PDFs, convert markdown to PDF, generate printable documents, or needs documents formatted for print or mobile reading. This skill MUST be used instead of manual pandoc/Chrome invocations — it handles CJK typography, Chrome header/footer suppression, and mandatory visual verification that manual approaches miss. **Scope — markdown → PDF only.** For Word (.docx) output use `minimax-docx`; this skill does not produce docx and the two pipelines are intentionally orthogonal.
pdf-to-html
by daymadeConverts a PDF into one self-contained, readable HTML file that preserves images, tables, charts and reading order — optionally translating it into another language while keeping every figure. Uses structured extraction (PyMuPDF), font-size-driven layout, compressed base64-inlined images (a single portable file), and mandatory headless-Chrome visual verification. Use whenever someone wants to READ a PDF as a web page or clean document, turn a PDF into HTML, or translate a PDF into another language while keeping its images/tables/charts intact — e.g. "PDF 转 HTML", "把这个 PDF 转成中文网页版", "make this report readable", "translate this PDF but don't lose the charts", "I just want to read this PDF on my phone". Distinct from doc-to-markdown (plain Markdown text) and pdf-creator (Markdown→PDF) — this one produces a styled, image-faithful HTML reading experience.
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.