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...
qa-phase
by popup-studio-aiQA Phase execution — L1-L5 test planning, generation, execution, and reporting for a single feature. For sprint-level QA (7-Layer dataFlowIntegrity / S1 gate across multiple features) use /sprint qa <sprintId> which delegates to sprint-qa-flow agent (v2.1.13). Triggers: qa phase, QA test, qa run, QA 실행, QAフェーズ, QA阶段, fase QA, phase QA, QA-Phase, fase QA.
zero-script-qa
by popup-studio-aiZero Script QA — test without scripts using structured JSON logging and Docker monitoring. Triggers: zero-script-qa, log testing, docker logs, QA, 제로 스크립트 QA.
bkit-templates
by popup-studio-aiPDCA + Sprint document templates — Plan, Design, Analysis, Report for individual features plus templates/sprint/{master-plan, prd, plan, design, iterate, qa, report}.template.md for sprint-level documents (v2.1.13). Triggers: template, plan document, design template, 템플릿, 문서 양식.
pdca-fast-track
by popup-studio-aiDaniel-mode fast-track auto-approves Checkpoint 1-8 when Trust ≥ 80, fastTrack on, Design doc exists. Else L2 + manual gates. Triggers: pdca fast-track, skip checkpoints, auto approve, 패스트 트랙, ファストトラック, 快速通道, vía rápida.
pdca-watch
by popup-studio-aiLive PDCA dashboard ticking every 30s — reads pdca-status.json + token-ledger.ndjson tail, renders fixed-width panel via CC /loop. Triggers: pdca watch, live dashboard, watch progress, 실시간 대시보드, ライブダッシュボード, 实时仪表板, panel en vivo, tableau de bord.
sprint
by popup-studio-aiSprint Management — generic sprint capability for ANY bkit user. 16 sub-actions: init, start, status, watch, phase, iterate, qa, report, archive, list, feature, pause, resume, fork, help, master-plan. Triggers: sprint, sprint start, sprint init, sprint status, sprint list, 스프린트, 스프린트 시작, 스프린트 상태, スプリント, スプリント開始, スプリント状態, 冲刺, 冲刺开始, 冲刺状态, sprint, iniciar sprint, estado sprint, sprint, demarrer sprint, statut sprint, Sprint, Sprint starten, Sprint Status, sprint, avviare sprint, stato sprint, master plan, multi-sprint plan, sprint master plan, 마스터 플랜, 멀티 스프린트 계획, 스프린트 마스터 플랜, マスタープラン, マルチスプリント計画, スプリントマスタープラン, 主计划, 多冲刺计划, 冲刺主计划, plan maestro, plan multi-sprint, plan maestro sprint, plan maître, plan multi-sprint, plan maître sprint, Masterplan, Multi-Sprint-Plan, Sprint-Masterplan, piano principale, piano multi-sprint, piano principale sprint.
desktop-app
by popup-studio-aiDesktop app development guide — Electron and Tauri for cross-platform apps. Triggers: desktop app, Electron, Tauri, mac app, windows app, 데스크톱 앱.
bkit
by popup-studio-aibkit plugin help - list available functions including /pdca (9-phase feature cycle), /sprint (8-phase feature container, v2.1.13), /control (Trust L0-L4 + SPRINT_AUTORUN_SCOPE), /bkit-explore, and 40+ specialized skills. Use "/bkit" or "bkit help". Triggers: bkit, help, functions, 도움말, 기능, ヘルプ, 帮助, ayuda, aide, Hilfe, aiuto.
bkit-evals
by popup-studio-aiRun skill evals via evals/runner.js — wrapper validates skill names, captures stdout/stderr, persists JSON results. Triggers: bkit evals, evals run, skill quality, eval runner, 스킬 평가, 評価実行, 评估运行, evaluación, évaluation.
bkit-explore
by popup-studio-aiBrowse installed bkit skills, agents, and evals via lib/discovery/explorer.js (filesystem scan, no subprocess). Triggers: bkit explore, list skills, skill discovery, browse skills, 스킬 탐색, スキル探索, 技能探索, explorar, explorer.
bkit-rules
by popup-studio-aiCore rules for bkit — PDCA methodology, level detection, agent triggering, quality standards, Sprint management (8-phase container with 4 auto-pause triggers, v2.1.13), and Trust Level scope (L0-L4 gates PDCA + Sprint auto-run). Triggers: bkit rules, core rules, methodology, 핵심 규칙, PDCA 규칙.
audit
by popup-studio-aiView audit logs, decision traces, and session history for AI transparency. ACTION_TYPES (19 entries) include PDCA events (phase_transition, gate_passed/failed, agent_spawned/completed/failed, rollback_executed, destructive_blocked) and Sprint events (sprint_paused, sprint_resumed, master_plan_created — v2.1.13). Triggers: audit, log, decision trace, history, 감사 로그, 결정 추적.
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.