Analyze construction drawings to extract dimensions, annotations, symbols, and metadata.

MITAuto-check passedMedia & Creative

Install Drawing Analyzer

skills CLI
$ npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill drawing-analyzer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction drawing-analyzer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git skills-src && mkdir -p .claude/skills && cp -r skills-src/2_DDC_Book/2.4-PDF-CAD-to-Data/drawing-analyzer .claude/skills/drawing-analyzer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
drawing-analyzer
GitHub stars
345
Used in
1 other repo
Token cost
~4k tokens
SKILL.md length
61 words
Files
3
Skills in repo
36
Repo updated
First seen
Licence
MIT

At a glance

Analyze construction drawings to extract dimensions, annotations, symbols, and metadata.

  • Tasks that involve Design review and critique
  • SKILL.md covers Overview, Business Case, Technical Implementation and Quick Start, plus 1 more section
  • Calls pip

What it does

Drawing Analyzer is an agent skill from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. Analyze construction drawings to extract dimensions, annotations, symbols, and metadata. Support quantity takeoff and design review automation.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `claw.json` and `instructions.md`).

It sits in Media & Creative, covering Design review and critique. The repository describes itself as: 221 AI skills for construction: BIM analysis, cost estimation, scheduling, document control, and automation with Claude Code. The licence is MIT.

When your agent uses it

  • Tasks that involve Design review and critique

Example prompts

  • “/drawing-analyzer”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit ce45bbf. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Drawing Analyzer loads about 4k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 61 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction at commit ce45bbf, republished under its MIT licence (© datadrivenconstruction). 61 words, ~3,992 tokens.

Download SKILL.mdSave it as .claude/skills/drawing-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
drawing-analyzer
description
Analyze construction drawings to extract dimensions, annotations, symbols, and metadata. Support quantity takeoff and design review automation.
homepage
https://datadrivenconstruction.io

Drawing Analyzer for Construction

Overview

Analyze construction drawings (PDF, DWG) to extract dimensions, annotations, symbols, title block data, and support automated quantity takeoff and design review.

Business Case

Drawing analysis automation enables:

  • Faster Takeoffs: Extract quantities from drawings
  • Quality Control: Verify drawing completeness
  • Data Extraction: Pull metadata for project systems
  • Design Review: Automated checking against standards

Technical Implementation

python
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
import re
import pdfplumber
from pathlib import Path

@dataclass
class TitleBlockData:
    project_name: str
    project_number: str
    sheet_number: str
    sheet_title: str
    discipline: str
    scale: str
    date: str
    revision: str
    drawn_by: str
    checked_by: str
    approved_by: str

@dataclass
class Dimension:
    value: float
    unit: str
    dimension_type: str  # linear, angular, radial
    location: Tuple[float, float]
    associated_text: str

@dataclass
class Annotation:
    text: str
    annotation_type: str  # note, callout, tag, keynote
    location: Tuple[float, float]
    references: List[str]

@dataclass
class Symbol:
    symbol_type: str  # door, window, equipment, etc.
    tag: str
    location: Tuple[float, float]
    properties: Dict[str, Any]

@dataclass
class DrawingAnalysisResult:
    file_name: str
    title_block: Optional[TitleBlockData]
    dimensions: List[Dimension]
    annotations: List[Annotation]
    symbols: List[Symbol]
    scale_factor: float
    drawing_area: Tuple[float, float]
    quality_issues: List[str]

class DrawingAnalyzer:
    """Analyze construction drawings for data extraction."""

    # Common dimension patterns
    DIMENSION_PATTERNS = [
        r"(\d+'-\s*\d+(?:\s*\d+/\d+)?\"?)",  # Feet-inches: 10'-6", 10' - 6 1/2"
        r"(\d+(?:\.\d+)?)\s*(?:mm|cm|m|ft|in)",  # Metric/imperial with unit
        r"(\d+'-\d+\")",  # Compact feet-inches
        r"(\d+)\s*(?:SF|LF|CY|EA)",  # Quantity dimensions
    ]

    # Common annotation patterns
    ANNOTATION_PATTERNS = {
        'keynote': r'^\d{1,2}[A-Z]?$',  # 1A, 12, 5B
        'room_tag': r'^(?:RM|ROOM)\s*\d+',
        'door_tag': r'^[A-Z]?\d{2,3}[A-Z]?$',
        'grid_line': r'^[A-Z]$|^\d+$',
        'elevation': r'^(?:EL|ELEV)\.?\s*\d+',
        'detail_ref': r'^\d+/[A-Z]\d+',
    }

    # Scale patterns
    SCALE_PATTERNS = [
        r"SCALE:\s*(\d+(?:/\d+)?)\s*[\"']\s*=\s*(\d+)\s*['\-]",  # 1/4" = 1'-0"
        r"(\d+):(\d+)",  # 1:100
        r"NTS|NOT TO SCALE",
    ]

    def __init__(self):
        self.results: Dict[str, DrawingAnalysisResult] = {}

    def analyze_pdf_drawing(self, pdf_path: str) -> DrawingAnalysisResult:
        """Analyze a PDF drawing."""
        path = Path(pdf_path)

        all_text = ""
        dimensions = []
        annotations = []
        symbols = []
        quality_issues = []

        with pdfplumber.open(pdf_path) as pdf:
            for page in pdf.pages:
                # Extract text
                text = page.extract_text() or ""
                all_text += text + "\n"

                # Extract dimensions
                page_dims = self._extract_dimensions(text)
                dimensions.extend(page_dims)

                # Extract annotations
                page_annots = self._extract_annotations(text)
                annotations.extend(page_annots)

                # Extract from tables (often contain schedules)
                tables = page.extract_tables()
                for table in tables:
                    symbols.extend(self._parse_schedule_table(table))

        # Parse title block
        title_block = self._extract_title_block(all_text)

        # Determine scale
        scale_factor = self._determine_scale(all_text)

        # Quality checks
        quality_issues = self._check_drawing_quality(
            title_block, dimensions, annotations
        )

        result = DrawingAnalysisResult(
            file_name=path.name,
            title_block=title_block,
            dimensions=dimensions,
            annotations=annotations,
            symbols=symbols,
            scale_factor=scale_factor,
            drawing_area=(0, 0),  # Would need image analysis
            quality_issues=quality_issues
        )

        self.results[path.name] = result
        return result

    def _extract_dimensions(self, text: str) -> List[Dimension]:
        """Extract dimensions from text."""
        dimensions = []

        for pattern in self.DIMENSION_PATTERNS:
            matches = re.findall(pattern, text)
            for match in matches:
                value, unit = self._parse_dimension_value(match)
                if value > 0:
                    dimensions.append(Dimension(
                        value=value,
                        unit=unit,
                        dimension_type='linear',
                        location=(0, 0),
                        associated_text=match
                    ))

        return dimensions

    def _parse_dimension_value(self, dim_text: str) -> Tuple[float, str]:
        """Parse dimension text to value and unit."""
        dim_text = dim_text.strip()

        # Feet and inches: 10'-6"
        ft_in_match = re.match(r"(\d+)'[-\s]*(\d+)?(?:\s*(\d+)/(\d+))?\"?", dim_text)
        if ft_in_match:
            feet = int(ft_in_match.group(1))
            inches = int(ft_in_match.group(2) or 0)
            if ft_in_match.group(3) and ft_in_match.group(4):
                inches += int(ft_in_match.group(3)) / int(ft_in_match.group(4))
            return feet * 12 + inches, 'in'

        # Metric with unit
        metric_match = re.match(r"(\d+(?:\.\d+)?)\s*(mm|cm|m)", dim_text)
        if metric_match:
            return float(metric_match.group(1)), metric_match.group(2)

        # Just a number
        num_match = re.match(r"(\d+(?:\.\d+)?)", dim_text)
        if num_match:
            return float(num_match.group(1)), ''

        return 0, ''

    def _extract_annotations(self, text: str) -> List[Annotation]:
        """Extract annotations from text."""
        annotations = []
        lines = text.split('\n')

        for line in lines:
            line = line.strip()
            if not line:
                continue

            for annot_type, pattern in self.ANNOTATION_PATTERNS.items():
                if re.match(pattern, line, re.IGNORECASE):
                    annotations.append(Annotation(
                        text=line,
                        annotation_type=annot_type,
                        location=(0, 0),
                        references=[]
                    ))
                    break

            # General notes
            if line.startswith(('NOTE:', 'SEE ', 'REFER TO', 'TYP', 'U.N.O.')):
                annotations.append(Annotation(
                    text=line,
                    annotation_type='note',
                    location=(0, 0),
                    references=[]
                ))

        return annotations

    def _extract_title_block(self, text: str) -> Optional[TitleBlockData]:
        """Extract title block information."""
        # Common title block patterns
        patterns = {
            'project_name': r'PROJECT(?:\s*NAME)?:\s*(.+?)(?:\n|$)',
            'project_number': r'(?:PROJECT\s*)?(?:NO|NUMBER|#)\.?:\s*(\S+)',
            'sheet_number': r'SHEET(?:\s*NO)?\.?:\s*([A-Z]?\d+(?:\.\d+)?)',
            'sheet_title': r'SHEET\s*TITLE:\s*(.+?)(?:\n|$)',
            'scale': r'SCALE:\s*(.+?)(?:\n|$)',
            'date': r'DATE:\s*(\d{1,2}[/-]\d{1,2}[/-]\d{2,4})',
            'revision': r'REV(?:ISION)?\.?:\s*(\S+)',
            'drawn_by': r'(?:DRAWN|DRN)\s*(?:BY)?:\s*(\S+)',
            'checked_by': r'(?:CHECKED|CHK)\s*(?:BY)?:\s*(\S+)',
        }

        extracted = {}
        for field, pattern in patterns.items():
            match = re.search(pattern, text, re.IGNORECASE)
            extracted[field] = match.group(1).strip() if match else ''

        # Determine discipline from sheet number
        sheet_num = extracted.get('sheet_number', '')
        discipline = ''
        if sheet_num:
            prefix = sheet_num[0].upper() if sheet_num[0].isalpha() else ''
            discipline_map = {
                'A': 'Architectural', 'S': 'Structural', 'M': 'Mechanical',
                'E': 'Electrical', 'P': 'Plumbing', 'C': 'Civil',
                'L': 'Landscape', 'I': 'Interior', 'F': 'Fire Protection'
            }
            discipline = discipline_map.get(prefix, '')

        return TitleBlockData(
            project_name=extracted.get('project_name', ''),
            project_number=extracted.get('project_number', ''),
            sheet_number=sheet_num,
            sheet_title=extracted.get('sheet_title', ''),
            discipline=discipline,
            scale=extracted.get('scale', ''),
            date=extracted.get('date', ''),
            revision=extracted.get('revision', ''),
            drawn_by=extracted.get('drawn_by', ''),
            checked_by=extracted.get('checked_by', ''),
            approved_by=''
        )

    def _parse_schedule_table(self, table: List[List]) -> List[Symbol]:
        """Parse schedule table to extract symbols/elements."""
        symbols = []

        if not table or len(table) < 2:
            return symbols

        # First row is usually headers
        headers = [str(cell).lower() if cell else '' for cell in table[0]]

        # Find key columns
        tag_col = next((i for i, h in enumerate(headers) if 'tag' in h or 'mark' in h or 'no' in h), 0)
        type_col = next((i for i, h in enumerate(headers) if 'type' in h or 'size' in h), -1)

        for row in table[1:]:
            if len(row) > tag_col and row[tag_col]:
                tag = str(row[tag_col]).strip()
                symbol_type = str(row[type_col]).strip() if type_col >= 0 and len(row) > type_col else ''

                if tag:
                    props = {}
                    for i, header in enumerate(headers):
                        if i < len(row) and row[i]:
                            props[header] = str(row[i])

                    symbols.append(Symbol(
                        symbol_type=symbol_type or 'unknown',
                        tag=tag,
                        location=(0, 0),
                        properties=props
                    ))

        return symbols

    def _determine_scale(self, text: str) -> float:
        """Determine drawing scale factor."""
        for pattern in self.SCALE_PATTERNS:
            match = re.search(pattern, text, re.IGNORECASE)
            if match:
                if 'NTS' in match.group(0).upper():
                    return 0  # Not to scale

                if '=' in match.group(0):
                    # Imperial: 1/4" = 1'-0"
                    return self._parse_imperial_scale(match.group(0))
                else:
                    # Metric: 1:100
                    return 1 / float(match.group(2))

        return 1.0  # Default

    def _parse_imperial_scale(self, scale_text: str) -> float:
        """Parse imperial scale to factor."""
        match = re.search(r'(\d+)(?:/(\d+))?\s*["\']?\s*=\s*(\d+)', scale_text)
        if match:
            numerator = float(match.group(1))
            denominator = float(match.group(2)) if match.group(2) else 1
            feet = float(match.group(3))
            inches_per_foot = (numerator / denominator)
            return inches_per_foot / (feet * 12)
        return 1.0

    def _check_drawing_quality(self, title_block: TitleBlockData,
                                dimensions: List, annotations: List) -> List[str]:
        """Check drawing for quality issues."""
        issues = []

        if title_block:
            if not title_block.project_number:
                issues.append("Missing project number in title block")
            if not title_block.sheet_number:
                issues.append("Missing sheet number")
            if not title_block.scale:
                issues.append("Missing scale indication")
            if not title_block.date:
                issues.append("Missing date")

        if len(dimensions) == 0:
            issues.append("No dimensions found - verify drawing content")

        # Check for typical construction notes
        note_types = [a.annotation_type for a in annotations]
        if 'note' not in note_types:
            issues.append("No general notes found")

        return issues

    def generate_drawing_index(self, results: List[DrawingAnalysisResult]) -> str:
        """Generate drawing index from multiple analyzed drawings."""
        lines = ["# Drawing Index", ""]
        lines.append("| Sheet | Title | Discipline | Scale | Rev |")
        lines.append("|-------|-------|------------|-------|-----|")

        for result in sorted(results, key=lambda r: r.title_block.sheet_number if r.title_block else ''):
            if result.title_block:
                tb = result.title_block
                lines.append(f"| {tb.sheet_number} | {tb.sheet_title} | {tb.discipline} | {tb.scale} | {tb.revision} |")

        return "\n".join(lines)

    def generate_report(self, result: DrawingAnalysisResult) -> str:
        """Generate analysis report for a drawing."""
        lines = ["# Drawing Analysis Report", ""]
        lines.append(f"**File:** {result.file_name}")

        if result.title_block:
            tb = result.title_block
            lines.append("")
            lines.append("## Title Block")
            lines.append(f"- **Project:** {tb.project_name}")
            lines.append(f"- **Project No:** {tb.project_number}")
            lines.append(f"- **Sheet:** {tb.sheet_number}")
            lines.append(f"- **Title:** {tb.sheet_title}")
            lines.append(f"- **Discipline:** {tb.discipline}")
            lines.append(f"- **Scale:** {tb.scale}")
            lines.append(f"- **Date:** {tb.date}")
            lines.append(f"- **Revision:** {tb.revision}")

        lines.append("")
        lines.append("## Content Summary")
        lines.append(f"- **Dimensions Found:** {len(result.dimensions)}")
        lines.append(f"- **Annotations Found:** {len(result.annotations)}")
        lines.append(f"- **Symbols/Elements:** {len(result.symbols)}")

        if result.quality_issues:
            lines.append("")
            lines.append("## Quality Issues")
            for issue in result.quality_issues:
                lines.append(f"- ⚠️ {issue}")

        if result.symbols:
            lines.append("")
            lines.append("## Elements Found")
            for symbol in result.symbols[:20]:
                lines.append(f"- {symbol.tag}: {symbol.symbol_type}")

        return "\n".join(lines)

Quick Start

python
# Initialize analyzer
analyzer = DrawingAnalyzer()

# Analyze a drawing
result = analyzer.analyze_pdf_drawing("A101_Floor_Plan.pdf")

# Check title block
if result.title_block:
    print(f"Sheet: {result.title_block.sheet_number}")
    print(f"Title: {result.title_block.sheet_title}")
    print(f"Scale: {result.title_block.scale}")

# Review extracted data
print(f"Dimensions: {len(result.dimensions)}")
print(f"Annotations: {len(result.annotations)}")
print(f"Symbols: {len(result.symbols)}")

# Check quality
for issue in result.quality_issues:
    print(f"Issue: {issue}")

# Generate report
report = analyzer.generate_report(result)
print(report)

Dependencies

bash
pip install pdfplumber

© datadrivenconstruction, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in 2_DDC_Book/2.4-PDF-CAD-to-Data/drawing-analyzer of datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.

  • SKILL.md
  • claw.json
  • instructions.md

Open the folder on GitHubat commit ce45bbf

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction, which our catalogue first saw on October 9, 2026.

Compare with similar skills

Drawing Analyzer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Drawing Analyzer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Drawing Analyzer this skilldatadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3451 repos~4kAutomated safety check: PassMIT
Architecture Client Presentationmohitagw15856/pm-claude-skills1.4k—~1.5kAutomated safety check: PassMIT
Consult ClaudeEpicenterHQ/epicenter4.8k—~2kAutomated safety check: PassCustom licence
System Atlasinkboard/system-atlas429—~2.3kAutomated safety check: PassMIT
Design Image Studiokangarooking/design-image-studio102—~1.5kAutomated safety check: PassMIT
Kicad Reviewmixelpixx/Konnect917—~3.2kAutomated safety check: PassAGPL-3.0

Similar skills

  • Architecture Client Presentation

    mohitagw15856/pm-claude-skills

    Present a design to a client so they can make a decision rather than react to a picture — the brief restated, the moves explained against it, the options with their trade-offs, and a specific…

    1.4k GitHub stars~1.5k tokensUpdated yesterday
    Media & CreativeAuto-check passed
  • Consult Claude

    EpicenterHQ/epicenter

    Assign Claude Code a read-only investigation, recommendation, or finished text draft.

    4.8k GitHub stars~2k tokensUpdated yesterday
    Media & CreativeAuto-check passed
  • System Atlas

    inkboard/system-atlas

    Build and maintain an explorable, progressively-disclosed isometric "atlas" of a system's architecture — an interactive page (hover to read, click to pin, go inside for steps, moving data packets…

    429 GitHub stars~2.3k tokensUpdated 1 mo ago
    Media & CreativeAuto-check passed
  • Design Image Studio

    kangarooking/design-image-studio

    Directly generate design-oriented AI images with strong creative direction and prompt engineering.

    102 GitHub stars~1.5k tokensUpdated 5 mo ago
    Media & CreativeAuto-check passed
  • Kicad Review

    mixelpixx/Konnect

    Design review and validation workflow for KiCAD projects via MCP tools.

    917 GitHub stars~3.2k tokensUpdated 4 days ago
    Media & CreativeAuto-check passed
  • Kicad

    aklofas/kicad-happy

    Analyze KiCad projects and PDF schematics: schematics, PCB layouts, Gerbers, footprints, symbols, netlists, and design rules.

    1.4k GitHub starsUsed in 1 repo~20k tokens
    Documents & OfficeAuto-check passed

More from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

All 36 skills in this repo
  • AI Agent Orchestration

    datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

    Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints.

    345 GitHub stars~679 tokensUpdated 1 mo ago
    Auto-check passed
  • Embodied Carbon Esg

    datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

    Estimate embodied carbon and produce ESG/climate reporting for construction: LCA per work item, material-based carbon factors, EU taxonomy and CSRD alignment.

    345 GitHub stars~664 tokensUpdated 1 mo ago
    Auto-check passed
  • Generative AI Design

    datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

    Generative design for construction: text-to-BIM concepts, option generation, and AI-assisted design iteration with cost and carbon feedback.

    345 GitHub stars~593 tokensUpdated 1 mo ago
    Auto-check passed
  • Material Passports Circular

    datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

    Material passports and circular construction: generate per-element material inventories from BOQ/BIM, mark reuse potential and recycled content, and prepare deconstruction data.

    345 GitHub stars~634 tokensUpdated 1 mo ago
    Auto-check passed
  • ML Model Retrainer

    datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

    Automated pipeline for retraining ML models with new construction data.

    345 GitHub stars~4.6k tokensUpdated 1 mo ago
    Auto-check passed
  • Oce Cost Browser

    datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

    Browse and search the OpenConstructionERP cost database: classification tree, SQL and semantic search, autocomplete, certainty badges, and the resource catalog.

    345 GitHub stars~637 tokensUpdated 1 mo ago
    Auto-check passed

Questions about Drawing Analyzer

What does Drawing Analyzer do?

Analyze construction drawings to extract dimensions, annotations, symbols, and metadata. Drawing Analyzer is an agent skill from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. Analyze construction drawings to extract dimensions, annotations, symbols, and metadata.

When should I use Drawing Analyzer?

Drawing Analyzer fits situations like: tasks that involve Design review and critique.

How do I install Drawing Analyzer in Claude Code?

Run `npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill drawing-analyzer -a claude-code`. Or copy the skill folder (2_DDC_Book/2.4-PDF-CAD-to-Data/drawing-analyzer in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction) into .claude/skills/drawing-analyzer in your project. Claude Code loads it when a task matches its description.

How do I install Drawing Analyzer in Codex?

Run `npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill drawing-analyzer -a codex`. Or copy the skill folder (2_DDC_Book/2.4-PDF-CAD-to-Data/drawing-analyzer in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction) into .agents/skills/drawing-analyzer in your project. Codex loads it when a task matches its description.

Can I use Drawing Analyzer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill drawing-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drawing-analyzer, .gemini/skills/drawing-analyzer, .github/skills/drawing-analyzer and .opencode/skills/drawing-analyzer in your project.

What does Drawing Analyzer need to run?

Going by SKILL.md and its folder, Drawing Analyzer needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Drawing Analyzer access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Drawing Analyzer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Drawing Analyzer use?

Drawing Analyzer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drawing Analyzer use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Drawing Analyzer?

Skills that share tags, products or a category with Drawing Analyzer: Architecture Client Presentation (mohitagw15856/pm-claude-skills, 1.4k stars), Consult Claude (EpicenterHQ/epicenter, 4.8k stars), System Atlas (inkboard/system-atlas, 429 stars) and Design Image Studio (kangarooking/design-image-studio, 102 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drawing Analyzer?

datadrivenconstruction (a GitHub user) maintains it in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction, which has 345 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on August 22, 2026.

Source: datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.