Terminal.skills
Skills/architectural-drawing-parser
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architectural-drawing-parser

Parse architectural drawings, floor plans, and building code compliance documents using Vision AI. Extracts building type, occupancy, floor areas, room layouts, dimensions, and code parameters. Use when: reading PDF floor plans, analyzing architectural drawings, extracting building data from images or scanned documents.

#architecture#vision-ai#floor-plan#building-codes#pdf-parsing
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed architectural-drawing-parser v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Create a responsive landing page layout following our brand guidelines"
  • "Review this component for accessibility issues and suggest fixes"

Information

Version
1.0.0
Author
terminal-skills
Category
Design
License
Apache-2.0

Documentation

Overview

Vision AI pipeline to extract structured building data from architectural drawings, floor plans, and IBC/IRC code compliance documents. Uses Claude's vision capabilities to read and interpret professional drawings, returning a normalized JSON object suitable for downstream 3D modeling or code validation workflows.

Supports IBC occupancy types (A-1 through U), construction types (I-A through V-B), sprinkler systems (NFPA 13/13R/13D), building dimensions, unit breakdowns, egress data, and floor plan elements (rooms, walls, doors, windows).

Instructions

Supported Drawing Types

Drawing TypeWhat Is Extracted
IBC/IRC code compliance drawingsOccupancy, construction type, heights, stories, areas, egress, units
Floor plans (unit-level)Rooms, dimensions, wall layouts, door/window positions
Site plansBuilding footprint, setbacks, parking
Building area analysis tablesUnit types, SF per unit, occupant loads, travel distances

Output Data Structure

The parser returns a BuildingData JSON object with these fields:

  • occupancy -- IBC occupancy type (e.g., "R-2", "A-2", "B")
  • constructionType -- IBC construction type (e.g., "V-B", "I-A")
  • sprinklerSystem -- "NFPA 13", "NFPA 13R", "NFPA 13D", or "None"
  • stories -- { permitted, actual }
  • height -- { permitted: { feet, meters }, actual: { feet, meters } }
  • totalBuildingArea -- { sqft, sqm }
  • units -- Array of { name, area: { sqft, sqm }, occupantLoad, loadFactor, count }
  • travelDistances -- Array of { floor, maximum: { feet, meters } }
  • scale -- Scale notation string (e.g., 1/16" = 1'-0")
  • rooms -- Array of { name, type, estimatedArea, dimensions } (floor plans only)

Parsing Approach

  1. Send the drawing image to Claude's vision API with a structured extraction prompt
  2. Request all building data as a single JSON object
  3. Convert all areas to both sqft and sqm (1 sqft = 0.0929 sqm)
  4. Convert all distances to both feet and meters (1 foot = 0.3048 m)
  5. Parse the JSON from the response text

Best Practices

  • Use 150 DPI or higher for scanned drawings
  • JPEG or PNG format; convert PDFs to images first (pdftoppm -jpeg -r 150 drawing.pdf output)
  • Process multi-sheet PDFs one page at a time, then merge results
  • Always verify extracted data against the source before structural calculations

Examples

Example 1: Parsing a Floor Plan PDF

A developer receives a scanned floor plan of a 2-bedroom apartment unit and needs room dimensions for a renovation estimate.

Input: apartment_unit_plan.jpg (scanned at 200 DPI, 1/4" = 1'-0" scale)

Extracted JSON:
{
  "rooms": [
    { "name": "Living Room", "type": "living", "estimatedArea": { "sqft": 240, "sqm": 22.3 }, "dimensions": { "width": 16, "depth": 15, "units": "feet" } },
    { "name": "Kitchen", "type": "kitchen", "estimatedArea": { "sqft": 120, "sqm": 11.1 }, "dimensions": { "width": 12, "depth": 10, "units": "feet" } },
    { "name": "Master Bedroom", "type": "bedroom", "estimatedArea": { "sqft": 168, "sqm": 15.6 }, "dimensions": { "width": 14, "depth": 12, "units": "feet" } },
    { "name": "Bedroom 2", "type": "bedroom", "estimatedArea": { "sqft": 132, "sqm": 12.3 }, "dimensions": { "width": 12, "depth": 11, "units": "feet" } },
    { "name": "Bathroom", "type": "bathroom", "estimatedArea": { "sqft": 48, "sqm": 4.5 }, "dimensions": { "width": 8, "depth": 6, "units": "feet" } }
  ],
  "scale": "1/4\" = 1'-0\""
}

The developer uses the room dimensions to calculate material quantities for flooring (708 sqft total) and wall paint coverage.

Example 2: Extracting Building Data from an IBC Compliance Drawing

An architect submits a code compliance sheet for a 3-story apartment building. The parser extracts all building classification and egress data.

Input: ibc_compliance_sheet.jpg (building area analysis table + egress diagram)

Extracted JSON:
{
  "occupancy": "R-2",
  "constructionType": "V-B",
  "sprinklerSystem": "NFPA 13",
  "stories": { "permitted": 4, "actual": 3 },
  "height": {
    "permitted": { "feet": 60, "meters": 18.29 },
    "actual": { "feet": 35, "meters": 10.67 }
  },
  "totalBuildingArea": { "sqft": 8910, "sqm": 827.9 },
  "units": [
    { "name": "Type A", "area": { "sqft": 834, "sqm": 77.5 }, "occupantLoad": 5, "loadFactor": "1/200 SF", "count": 6 },
    { "name": "Type B", "area": { "sqft": 645, "sqm": 59.9 }, "occupantLoad": 4, "loadFactor": "1/200 SF", "count": 6 }
  ],
  "travelDistances": [
    { "floor": "Level 1", "maximum": { "feet": 66, "meters": 20.1 } },
    { "floor": "Level 2", "maximum": { "feet": 66, "meters": 20.1 } },
    { "floor": "Level 3", "maximum": { "feet": 66, "meters": 20.1 } }
  ]
}

This data feeds into the ibc-building-codes skill for compliance validation and the spec-to-3d skill for 3D model generation.

Guidelines

  • Accuracy depends on drawing quality and image resolution; low-res scans may produce incorrect dimensions
  • Very small text (title blocks, fine notes) may be misread -- zoom in for detail drawings
  • Complex overlapping hatching or linework may confuse room detection
  • Proprietary symbols or non-standard abbreviations may not be recognized
  • Always treat extracted data as an estimate; verify critical measurements manually
  • For multi-sheet sets, parse each sheet separately and merge the structured data
  • The parser works best with US-standard architectural drawings; metric-only drawings may need prompt adjustments