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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haze-blur
by Kototoro-appExpert guidance on Haze blur effect configuration, styling, materials, progressive blur, masking, and input scale. Use when configuring blur radius, tint/color effects, noise, HazeMaterials/CupertinoMaterials/FluentMaterials, gradient blurs, or blur masking.
ux-research
by mkurmanUse this skill when planning user research, conducting usability tests, creating journey maps, or designing A/B experiments. Triggers on user interviews, usability testing, user journey maps, A/B test design, survey design, persona creation, card sorting, tree testing, and any task requiring user experience research methodology or analysis.
ux-designer
by modu-ai우선순위별 UX 개선 권고안과 평가 보고서(휴리스틱·접근성·사용자 플로우)를 만들어 드립니다. 다음과 같은 요청 시 사용하세요: - "UX 디자인 검토해줘" - "휴리스틱 평가해줘" - "접근성(WCAG) 검토해줘" - "사용자 플로우 분석해줘" - "와이어프레임 사용성 점검해줘" - "사용성 개선안 정리해줘" 3개 차원(휴리스틱·접근성·플로우)을 분석해 심각도·우선순위가 매겨진 개선 로드맵으로 정리하고, ai-slop-reviewer·humanize-korean으로 마무리할 수 있습니다. [책임 경계] vs moai-product:ux-researcher: ux-designer=휴리스틱·접근성·플로우 평가, ux-researcher=인터뷰·페르소나·VOC 리서치
ux-researcher
by modu-ai사용자 인터뷰 가이드·페르소나 문서·VOC 분석 보고서·NPS 해석안 같은 UX 리서치 결과물을 만들어 드립니다. 다음과 같은 요청 시 사용하세요: - "사용자 인터뷰 질문지 만들어줘" - "VOC 분석해줘" - "페르소나 설계해줘" - "NPS 결과 해석해줘" - "유저빌리티 테스트 시나리오 만들어줘" - "고객 설문 인사이트 정리해줘" 정성·정량 리서치 결과를 의사결정에 쓸 수 있는 인사이트로 정리하고, ai-slop-reviewer·humanize-korean으로 마무리할 수 있습니다. [책임 경계] vs moai-product:ux-designer: ux-researcher=정성/정량 리서치(인터뷰·페르소나·VOC), ux-designer=디자인 평가(휴리스틱·접근성·플로우)
user-research
by yonatangrossUser personas, customer journey maps, interview guides, usability testing, and card sorting. Use when building user understanding, mapping customer experiences, planning user research sessions, or defining Jobs-to-Be-Done.
ux-researcher
by zouyangxiaohao111Expert user experience researcher specializing in user behavior analysis, usability testing, and data-driven design insights. Provides actionable research findings that improve product usability and user satisfaction
brainstorming
by jwyniaExpand seeds and escape convergent ideation. Use when you have the start of an idea and want to grow it, when brainstorming produces the same ideas every time, or when you need to explore possibility space.
tabletop-rpg-design
by omer-metinExpert system designer for tabletop roleplaying games covering dice mechanics, character creation, combat systems, narrative frameworks, GM tools, and playtesting methodologyUse when "tabletop rpg, ttrpg design, dice mechanics, character creation system, combat system design, gm tools, pbta, powered by the apocalypse, forged in the dark, blades in the dark, osr design, old school renaissance, narrative rpg, rules-light rpg, crunchy system, session zero, safety tools, x-card, fail forward, fiction first, player-facing rolls, advantage disadvantage, target number, dice pool, tabletop, rpg, game-design, dice-mechanics, pbta, osr, narrative-games, ttrpg, gm-tools, character-creation, blades-in-the-dark, forged-in-the-dark" mentioned.
thematic-map
by zzhongleiCreate well-designed maps that follow standard cartographic conventions. Use this skill when you need to create a map. If the map requires GIS or remote sensing data processing, complete all data preparation and processing steps BEFORE reading this skill. Only read this skill when the data is fully ready and you are about to begin composing the map.
evaluate
by ghaidaStructured UX evaluation that produces quantitative assessments, identifies specific issues, and routes to the right Intent skill for resolution. Part of the Intent design strategy system. Runs heuristic evaluations, cognitive walkthroughs, anti-pattern detection, and task success analysis. Scores, categorizes, and prioritizes findings — then maps every issue to the skill that fixes it. Trigger on: UX review, design audit, heuristic evaluation, usability assessment, "review this design", "what's wrong with this", "evaluate the experience", "is this accessible", "check for dark patterns", "how good is this UX", "rate this design", "find the problems", or any request to systematically assess the quality of a user experience. This is the diagnostic entry point of the Intent system — the UX doctor that diagnoses issues and refers to specialists.
blueprint
by ghaidaMap, analyze, and redesign the systems behind product experiences. Part of the Intent design strategy system. Creates service blueprints, ecosystem maps, process architecture, and dependency diagrams. Understands how services, teams, tools, and data flows connect to produce (or fail to produce) user outcomes. Proposes structural changes to how products and services are organized. Trigger on: service blueprints, system maps, process architecture, actor/role mapping, dependency analysis, cross-functional workflows, operational design, "how does this system work?", "what breaks when X happens?", "map out the service", "where are the dependencies?", or any question about the structural machinery behind a product experience. Use this skill broadly — whenever someone needs to understand or redesign how a system works, not just what a user sees.
fortify
by ghaidaHarden designs for real-world use by systematically identifying and designing for every condition outside the happy path. Part of the Intent design strategy system. Covers state inventories, error recovery, empty states, loading patterns, first-run experiences, stress testing, internationalization readiness, and latency handling. Trigger on: edge cases, error states, empty states, loading states, first-run experience, onboarding, offline mode, "what happens when", "what if the user", "stress test this", "what could go wrong", "harden this design", "edge case review", "what are the failure modes", zero states, timeout handling, or any question about how a design behaves outside ideal conditions. The happy path is a fantasy — this skill designs for the world your users actually live in.
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.