Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill drawing-analyzergit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/drawing-analyzer)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/drawing-analyzer"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/drawing-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/drawing-analyzer"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/drawing-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.03754 |
| Opus 5 | $0.00014 | $0.01877 |
| Sonnet 5 | $0.00005 | $0.00751 |
| Haiku 4.5 | $0.00003 | $0.00375 |
Grade A, and why
drawing-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- drawing-analyzer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 456 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 456 lines · 27 tokens per session scan A ca15ca72c52d
drawing-analyzer is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 20d ago), licensed MIT. It adds 27 tokens to every session and 3,754 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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