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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adu-targeted-page-viewer
by mikeOnBreezeExtracts construction plan PDFs into page PNGs, reads the sheet index to build a sheet-to-page manifest, and enables targeted viewing of specific sheets. This skill should be used when a corrections letter references specific plan sheets (e.g., "Sheet A3", "Detail 2/S3.1") and those sheets need to be located and analyzed within the PDF binder. Much faster than full plan extraction — builds the sheet manifest in under 2 minutes, then individual sheet lookups are instant. Triggers when a plan PDF needs to be navigated by sheet reference, or when the corrections interpreter needs to look at specific pages.
cwicr-takeoff-helper
by datadrivenconstructionAssist with quantity takeoff using CWICR data. Calculate quantities from dimensions, apply waste factors, and suggest related work items.
cwicr-unit-converter
by datadrivenconstructionConvert between construction measurement units. Handle metric/imperial conversion, area/volume calculations, and unit normalization for CWICR data.
autocad-expert
by theneoaiAutoCAD工程制图：2D图纸、图层、标注。Use when creating engineering drawings. Triggers: 'AutoCAD', '工程制图', 'CAD'. Works with: Claude Code, Codex, OpenCode, Cursor, Cline, OpenClaw, Kimi.
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。
parking-check
by shuotao停車場檢討：停車位淨空高度檢查（>210cm）與停車位數量分類統計（法定、無障礙、增設等八類）。觸發條件：使用者提到停車場、停車位、車位淨空、車道寬度、parking、clearance、機車位、無障礙車位。工具：get_rooms_by_level、query_elements_with_filter、override_element_graphics、get_field_values。
stair-hidden-line
by shuotao剖面隱藏樓梯可視化：在剖面視圖中，自動為被側板遮擋的樓梯梯級繪製虛線詳圖線。觸發條件：使用者提到樓梯隱藏線、stair hidden line、剖面樓梯、虛線、梯級、stair visualization、組合式樓梯。工具：trace_stair_geometry、create_detail_lines、get_line_styles。
architectural-dimensions
by TerminalSkillsReference for architectural dimensions, proportions, and building codes for AI-generated 3D/2D models. Use when: generating floor plans, creating 3D buildings, validating architectural designs, ensuring correct human-scale proportions in models.
ibc-building-codes
by TerminalSkillsReference for US International Building Code (IBC) occupancy types, construction types, height/area limits, egress requirements, and R-2 residential standards. Use when: generating US-compliant building models, validating building designs against IBC, understanding occupancy classifications and construction types.
pie-blueprint-engine
by TibsfoxGenerates engineering-standard drawings (P&ID, SLD, floor plans, isometric) with ISA-5.1 and IEEE symbol libraries in SVG/DXF format. Activates for drawing generation, diagram creation, blueprint packaging, and output formatting from verified engineering calculations.
common-architecture-diagramming
by ComeOnOliverStandards for creating clear, effective, and formalized software architecture diagrams (C4, UML). (triggers: ARCHITECTURE.md, **/*.mermaid, **/*.drawio, diagram, architecture, c4, system design, mermaid)
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.