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...
smoke-exhaust
by shuotao排煙窗法規檢討：無窗居室判定、無開口樓層判定、排煙有效面積計算、剖面標註、Excel 報告匯出。觸發條件：使用者提到排煙、排煙窗、無窗居室、無開口樓層、建技規§101、消防§188、天花板下80cm、有效開口、煙層。工具：check_smoke_exhaust_windows、check_floor_effective_openings、create_section_view、create_detail_lines、create_filled_region、create_text_note、export_smoke_review_excel。
auto-dimension
by shuotao自動標註尺寸：使用 Ray-Casting 或 BoundingBox 方法，在平面視圖中自動建立房間、走廊、MEP 設備的尺寸標註。觸發條件：使用者提到標註、尺寸、dimension、annotation、淨寬、淨高、measurement、自動標註、批次標註。工具：create_dimension_by_ray、create_dimension_by_bounding_box、get_room_info。
build-revit
by shuotaoBuild the RevitMCP Revit add-in for one or all Revit versions (2022-2026) using the unified RevitMCP.csproj
building-compliance
by shuotao建築法規檢討：居室採光比（第 41 條）、容積率與樓地板面積計算、停車位尺寸與數量檢核。觸發條件：使用者提到採光、daylight、容積率、FAR、樓地板面積、建蔽率、停車、parking、法規檢討、送審、regulatory。工具：get_room_daylight_info、query_elements_with_filter、get_rooms_by_level。
claude-md-sync
by shuotaoCLAUDE.md 雙向同步驗證：當合併外部 PR、Domain 升級 Skill、或 Tools 異動後，執行正向（作業→CLAUDE.md）與反向（CLAUDE.md→作業）的比對與修正迴圈。觸發條件：claude.md 同步、sync、驗證一致性、merge 後檢查、skill 新增後。
curtain-wall
by shuotao帷幕牆面板配置：設計帷幕牆面板排列模式並透過網頁預覽確認後套用到 Revit。觸發條件：使用者提到帷幕牆、curtain wall、面板、panel pattern、立面設計、facade design、玻璃面板、curtain grid、面板排列。工具：get_curtain_wall_info、get_curtain_panel_types、apply_panel_pattern。
dependent-view-crop
by shuotao從屬視圖批次裁剪：依網格線為邊界，批次建立從屬視圖並設定裁剪範圍。適用於大型專案分區出圖。觸發條件：使用者提到從屬視圖、dependent view、分區出圖、網格裁剪、視圖分割、batch crop。工具：get_all_grids、get_all_views、get_active_view。
detail-component-sync
by shuotao2D 詳圖元件同步：將詳圖圖頭（AE-numbering）的編號與圖紙號碼自動同步。觸發條件：使用者提到詳圖同步、圖頭、detail header、AE-numbering、圖紙編號同步、詳圖元件、detail component。工具：get_detail_components、sync_detail_component_numbers、create_detail_component_type、list_family_symbols。
detect-clashes
by shuotaoMEP 管線與結構（CSA）碰撞偵測，使用 Curve-to-Solid 策略進行干涉分析、視覺化與報告匯出。 TRIGGER when: 碰撞, 干涉, clash, MEP, 管線穿牆, 套管, 穿越, penetration, 碰撞偵測, 管線衝突
element-coloring
by shuotao元素上色工作流程：根據參數值對 Revit 元素進行顏色標記與視覺化。觸發條件：使用者提到上色、顏色標示、color code、highlight、視覺化標記、參數上色。工具：get_category_fields、get_field_values、query_elements_with_filter、override_element_graphics、clear_element_override、unjoin_wall_joins、rejoin_wall_joins。
element-query
by shuotao元素查詢與視覺化：三階段查詢協議（探索→對齊→擷取），支援依參數篩選與上色標記。觸發條件：使用者提到查詢、篩選、element query、filter、參數查詢、color-code、元素屬性、find elements。工具：get_active_schema、get_category_fields、get_field_values、query_elements_with_filter、override_element_graphics、clear_element_override。
fire-safety-check
by shuotao消防安全檢討：防火時效視覺化、走廊防火分析、外牆開口距離檢討（第 45 條、第 110 條）。觸發條件：使用者提到防火、耐燃、fire rating、防火時效、走廊、corridor、逃生、外牆開口、鄰地距離、Article 45、Article 110、防火區劃。工具：override_element_graphics、check_exterior_wall_openings、query_elements_with_filter。
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.