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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vkc-repo-guardrails
by LEE-SANG-BOKEnforce Viet K-Connect non-negotiables (no Server Actions, API Routes only, Drizzle/Supabase patterns, TanStack Query repo layer, i18n ko/en/vi key parity). Use before/after code changes and for PR review checklists. (키워드= 서버 액션 금지, API 라우트만, Drizzle, Supabase, TanStack Query, i18n 키 동기화)
vkc-seo-metadata
by LEE-SANG-BOKStandardize Next.js metadata in VKC (generateMetadata, buildPageMetadata, canonical, alternates, OG, Twitter, robots/noindex). Use when changing SEO, sitemap, robots, or social previews. (키워드= SEO, 메타데이터, 캐노니컬, alternates, hreflang, 오픈그래프, robots, noindex, sitemap)
vkc-api-route-pattern
by LEE-SANG-BOKStandardize Next.js App Router API route implementations under src/app/api/** (auth/session, input validation, Drizzle queries, rate limiting, response shape). Use when creating or refactoring API routes in this repo. (키워드= API 라우트, route.ts, 세션/권한, 입력 검증, Zod, validateBody, Drizzle, 레이트리밋)
vkc-datadog-observability
by LEE-SANG-BOKDatadog observability workflow for VKC — MCP triage (plugin-datadog-datadog), env/instrumentation setup, monitor contract, and privacy-safe logging. Use when debugging production issues, setting up Datadog, investigating errors/metrics/traces, or running observability automations. (키워드= Datadog, observability, MCP, us5, 모니터, 로그, 트레이스, APM, DD_API_KEY)
vkc-shared-memory-router
by LEE-SANG-BOKEnforce the VKC shared memory read order and subagent routing policy so every clean-workspace session, automation, and subagent starts from the same SoT before acting. Use for supervisor automations, repo exploration, multi-agent coordination, or when durable context must be shared across sessions. (키워드=shared memory, subagent router, read order, context memory, automation prompt, supervisor, explorer, worker)
vkc-skill-router
by LEE-SANG-BOKRoute VKC tasks to the correct domain skill from manifest.json. Always start with guardrails, shared-memory-router, loop-supervisor; then pick one domain skill by path/task keywords. (키워드=skill router, manifest, domain skill, which skill)
vkc-loop-supervisor
by LEE-SANG-BOKEnforce the VKC long-running supervisor loop: repo-local preflight, SoT read order, Desktop-outside clean workspace policy, backlog selection priority, patch-only mutation, and fail-closed execution. Use for supervisor automations, ongoing Codex development cycles, or when the user wants this repo to follow a persistent loop. (키워드=supervisor, hypothesis loop, preflight, patch-only, review packet, clean workspace, fail-closed)
vkc-moderation-trust
by LEE-SANG-BOKAI moderation, trust scoring, answer-accuracy, and keyword flag queue workflow for VKC UGC. Use when changing content-filter, moderation queues, trust badges, or answer-accuracy review. (키워드= moderation, trust, keywordFlagQueue, answer-accuracy, content-filter, WO-009)
vkc-perf-budget
by LEE-SANG-BOKPerformance and bundle-size budget workflow for VKC (perf:collect, perf:compare, perf:gate, bundle size, shared first load js). Use when changes may affect build output size or performance. (키워드= 성능, 번들 사이즈, 퍼포먼스, perf:gate, shared first load js, 빌드 메트릭)
vkc-regulation-knowledge-updater
by LEE-SANG-BOKBuild the regulation/knowledge update pipeline (official sources -> snapshots -> structured rulesets/templates -> admin approval -> active). Use for keeping visa rules and document requirements up-to-date without code hardcoding. (키워드= 규정 최신화, 공식 정보, 스냅샷, 검수/승인, 룰셋/템플릿 업데이트)
vkc-shared-memory-router
by LEE-SANG-BOKEnforce the VKC shared memory read order and subagent routing policy so every clean-workspace session, automation, and subagent starts from the same SoT before acting. Use for supervisor automations, repo exploration, multi-agent coordination, or when durable context must be shared across sessions. (키워드=shared memory, subagent router, read order, context memory, automation prompt, supervisor, explorer, worker)
vkc-ux-audit
by LEE-SANG-BOKRun a VKC UX expert audit (Nielsen-style heuristic review + mobile-first flow check) and produce a prioritized issue list with severity and fixes. (키워드= UX 감사, 휴리스틱, 모바일 QA, 접근성, 사용성)
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.