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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tech-claim-rubric
by toongriUse when evaluating technical claims in high-depth content section units (예: 문제 해결 / 상세 프로젝트 / 경력 기술서). Defines the 5-axis framework (A1 Technical Credibility, A2 Causal Honesty, A3 Outcome Presence & Clarity, A4 Ownership & Scope, A5 Scanability) plus 2 critical authenticity rules (R-Phys, R-Cross) used by tech-claim-examiner agent. Verb-scope inflation (previously a separate rule) is now caught by A4 integrity_suspected sub-flag (see a4-ownership-scope.md).
pin-audit
by toongriUse when checking pin graph health. Runs lib/pins/audit to detect dangling relations, duplicates, invalid entities, stale entries, and orphans, then presents a ranked report.
create-slides
by toongri깔끔하고 전문적인 HTML 기반 발표자료를 단일 파일로 생성한다. 수직 스크롤 + scroll-snap 방식의 스크롤텔링 프레젠테이션으로, 슬라이드 라이브러리 없이 순수 HTML+CSS로 구현한다. 다크/라이트 테마, highlight.js 코드 블럭, 디자인 시스템을 지원하며 frontend-design 스킬 연동으로 화려한 비주얼도 선택 가능하다. 트리거: "make a presentation", "create slides", "build a deck", "발표자료", "프레젠테이션", "슬라이드", "제안서", "발표 만들어", "ppt", "keynote", "pitch deck", "tech talk", "발표 만들어줘".
collect-jd
by toongriUse when collecting, curating, or organizing job descriptions (JDs) — triggers include "JD 모으고 있어", "JD 수집", "JD 큐레이션", "JD 정리하고 있어", "오늘 수집 정리해줘", "오늘 본 JD", "관리 중인 JD", "쌓아둔 JD", "내 프로필에 맞는 JD 쌓아줘", "내 이력에 맞는 JD 큐레이션", and "싹 돌려" (in JD rescan context). Do NOT trigger on discovery phrases claimed by resume-apply ("JD 찾아줘", "JD 골라줘", "공고 뭐 있지", "지원할 곳", "어디 넣을까") — those belong to resume-apply. Skill maintains project-scoped state at `$OMT_DIR/collect-jd/` (never global).
resume-apply
by toongriMUST USE this skill when ANY of these appear: (1) a JD or job posting is present — look for keywords like 채용, 채용공고, 주요업무, 자격요건, Requirements, Qualifications, 우대사항, 포지션; (2) user mentions applying to a specific company — '지원', '지원하려고', '지원 준비', '이력서 지원', 'resume apply', 'apply'; (3) user wants to tailor resume for a position — '이력서 맞춤', '이력서 준비', 'JD 이력서', 'JD 기반', 'JD 보고'; (4) user provides a JD via text, file path, or URL; (5) user mentions a company name with intent to apply (e.g. '토스 지원', '네이버 준비', '카카오 이력서'); (6) user wants to find or browse JDs — '어디 넣을까', '지원할 곳', 'JD 찾아', 'JD 골라', '공고 뭐 있지', '지원 준비 시작'. This skill handles the FULL workflow: acquire JD (provided or discovered from configured source) → create branch ({company}/{YYMMDD}) → tailor resume via review-resume → commit → generate PDF → deliver to configured output. Do NOT confuse with review-resume (general review without a target JD) or simple _config.yml edits.
resume-forge
by toongriUse when creating, sourcing, or refining resume problem-solving material — building compound scenarios, developing problem definitions, crafting solution strategies, or iterating entries until examiner approval. Triggers on 이력서 재료/소재, 문제 상황 만들기, 시나리오 작성, compound scenario, 이력서 항목 작성
review-resume
by toongriUse when the user asks to review, evaluate, check, or get feedback on their resume — even partially (self-introduction/자기소개, career/경력, problem-solving/문제해결, or any single section). Triggers on resume review/evaluation/feedback (이력서 리뷰/검토/피드백), section-specific evaluation requests, interview readiness checks, achievement line quality, AI tone audit, or _config.yml + review intent. When a JD is provided, evaluates JD fit and recommends optimal content from the candidate pool. NOT for simple _config.yml edits, PDF generation, layout/CSS changes, or interview prep.
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.