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...
insane-search
by fivetakuAuto-bypass for blocked websites — tries every method until one works. Use when WebFetch returns 402/403/blocked, or when accessing X/Twitter, Reddit, YouTube, GitHub, Mastodon, Medium, Substack, Stack Overflow, Threads, Naver, Coupang, LinkedIn, or any platform with WAF/bot protection. Leverages yt-dlp (1,858 media sites), Jina Reader, public APIs (HN, Bluesky, arXiv), and a generic WAF-profile-driven fetch chain (curl_cffi TLS impersonation, mobile URL transforms, Playwright real-Chrome) with auto dependency install. Korean triggers: 트위터/X 못 열어, 레딧 안 읽혀, 유튜브 자막 뽑아줘, 깃헙 검색, 사이트 차단됨, 스레드 안 열려, 마스토돈, 미디엄, 서브스택, 스택오버플로우, 네이버 블로그, 디시인사이드, 에펨코리아, 요즘IT, 긱뉴스, 클리앙, 쿠팡, 링크드인, 당근마켓. English triggers: twitter access, reddit blocked, youtube subtitles, github search, arxiv papers, threads, mastodon, medium, substack, stackoverflow, naver blog, dcinside, fmkorea, coupang, linkedin, yozm, wishket. Do NOT trigger for simple web searches that WebSearch can handle directly.
vibe-sunsang-retro
by fivetakuConversation-log converter — transforms Claude Code JSONL logs into Markdown and provides an analysis guide. Korean triggers: "변환", "대화 변환", "로그 변환", "회고", "이번 주 대화". English triggers: "retro", "convert conversations", "log conversion".
vibe-sunsang-knowledge
by fivetakuVibe-sunsang knowledge base — answers questions about the level system (v2 6-axis × 7 levels), anti-patterns, and workspace types. Korean triggers: "바선생 안티패턴이 뭐야?", "바선생 레벨 시스템 설명해줘", "6축이 뭐야?", "바선생 성장 지표". English triggers: "vibe-sunsang levels", "anti-patterns", "workspace types".
vibe-sunsang-growth
by fivetakuGrowth report generator — analyzes AI-collaboration session data and produces a progression report using the v2 level system (6 axes × 7 levels, 0.5 increments). Korean triggers: "성장 리포트", "성장 분석", "얼마나 성장했는지", "레벨 체크", "성장 트래킹". English triggers: "growth report", "growth tracking", "level check".
vibe-sunsang-mentor
by fivetakuAI-collaboration mentoring — coaches users on request quality, anti-patterns, and concepts across 4 modes, analyzed via v2 level system (6 axes × 7 levels, 0.5 increments). Korean triggers: "멘토링해줘", "코칭해줘", "요청 코칭해줘", "뭘 잘못하고 있는지", "어떻게 요청하면 좋을지". English triggers: "mentor", "coach", "coaching", "improve my requests".
vibe-sunsang-onboard
by fivetakuVibe-sunsang initial setup — guides through workspace creation, project linking, type classification, and first conversion. Korean triggers: "바선생 시작", "온보딩", "초기 설정", "초기화", "셋업". English triggers: "onboarding", "init", "setup", "start vibe-sunsang".
content-pipeline
by fivetakuThis skill should be used when the user asks to "콘텐츠 만들어줘", "카드뉴스 만들어줘", "카드뉴스 영상 만들어줘", "리서치부터 영상까지", "콘텐츠 파이프라인", "content pipeline", "주제로 콘텐츠 만들어줘". 주제 하나로 리서치→카드뉴스→영상까지 풀 파이프라인을 자동 실행합니다. Make sure to use this skill whenever the user mentions content creation that involves research, card news, or video generation from a topic.
audit-xls
by fivetakuAudit a spreadsheet for formula accuracy, errors, and common mistakes. Scopes to a selected range, a single sheet, or the entire model, including financial-model integrity checks like BS balance, cash tie-out, and logic sanity. Triggers on "audit this sheet", "check my formulas", "find formula errors", "QA this spreadsheet", "sanity check this", "debug model", "model check", "model won't balance", "something's off in my model", and "model review".
deck-refresh
by fivetakuUpdates a presentation with new numbers — quarterly refreshes, earnings updates, comp rolls, rebased market data. Use whenever the user asks to "update the deck with Q4 numbers", "refresh the comps", "roll this forward", "swap in the new earnings", "change all the $485M to $512M", or any request to swap figures across an existing deck without rebuilding it.
ib-check-deck
by fivetakuInvestment banking presentation quality checker. Reviews a pitch deck or client-ready presentation for (1) number consistency across slides, (2) data-narrative alignment, (3) language polish against IB standards, (4) visual and formatting QC. Use whenever the user asks to review, check, QC, proof, or do a final pass on a deck, pitch, or client materials — including requests like "check my numbers", "reconcile figures across slides", "is this client-ready", or "what am I missing before I send this out".
dcf-model
by fivetakuReal DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.
comps-analysis
by fivetakuBuild institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format.
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.