specification-extractor
Extract structured data from construction specifications. Parse CSI sections, requirements, submittals, and product data from spec documents.
Install / Use
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill specification-extractorInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Content & MediaSupported Platforms
Our assessment of specification-extractor
specification-extractor scores 89/100 on our quality scale, 562nd of 1,173 Content & Media skills we index (top 48%).
Its SKILL.md is 14 KB long, well organised into 11 sections with 3 code examples: a thorough specification that gives an agent plenty to work with.
It has 333 GitHub stars, a meaningful sign that others use it.
Maintenance, license and trust
- The repository was last updated about 2 months ago, so specification-extractor is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
specification-extractor compared with similar skills
All 4 of these similar skills score higher than specification-extractor; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| specification-extractor (this skill)by datadrivenconstruction | 89 | 333 | 2mo ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 106.4k | 5d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.3k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 90.4k | today | MCP Server |
| crawl4aiby unclecode | 100 | 86.6k | today | MCP Server |
Frequently asked questions
- How do I install specification-extractor?
- Run
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill specification-extractor. The install tabs above show the steps for each supported agent. - Which AI agents does specification-extractor work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is specification-extractor safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is specification-extractor still maintained?
- The repository was last updated about 2 months ago, so specification-extractor is actively maintained.
Skill content
View source on GitHubname: "specification-extractor" description: "Extract structured data from construction specifications. Parse CSI sections, requirements, submittals, and product data from spec documents." homepage: "https://datadrivenconstruction.io" metadata: {"openclaw": {"emoji": "📑", "os": ["darwin", "linux", "win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}
Specification Extractor for Construction
Overview
Extract structured data from construction specification documents. Parse CSI MasterFormat sections, identify requirements, submittals, product standards, and compile actionable data for estimating and procurement.
Business Case
Automated spec extraction enables:
- Faster Estimating: Quickly identify scope and requirements
- Procurement Accuracy: Extract exact product specifications
- Submittal Tracking: Identify all required submittals
- Compliance Checking: Verify specs against standards
Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional
import re
import pdfplumber
from pathlib import Path
@dataclass
class SpecSection:
number: str # e.g., "03 30 00"
title: str
part1_general: Dict[str, Any]
part2_products: Dict[str, Any]
part3_execution: Dict[str, Any]
raw_text: str
@dataclass
class ProductRequirement:
section: str
manufacturer: str
product_name: str
model: str
standards: List[str]
properties: Dict[str, str]
@dataclass
class SubmittalRequirement:
section: str
submittal_type: str # shop drawings, samples, product data, etc.
description: str
timing: str
copies: int
@dataclass
class SpecExtractionResult:
document_name: str
total_pages: int
sections: List[SpecSection]
products: List[ProductRequirement]
submittals: List[SubmittalRequirement]
standards_referenced: List[str]
class SpecificationExtractor:
"""Extract structured data from construction specifications."""
# CSI MasterFormat patterns
CSI_SECTION_PATTERN = r'^(\d{2}\s?\d{2}\s?\d{2})\s*[-–]\s*(.+?)$'
PART_PATTERN = r'^PART\s+(\d+)\s*[-–]\s*(.+?)$'
ARTICLE_PATTERN = r'^(\d+\.\d+)\s+([A-Z][A-Z\s]+)$'
# Submittal type keywords
SUBMITTAL_TYPES = {
'shop drawings': 'Shop Drawings',
'product data': 'Product Data',
'samples': 'Samples',
'certificates': 'Certificates',
'test reports': 'Test Reports',
'manufacturer instructions': 'Manufacturer Instructions',
'warranty': 'Warranty',
'maintenance data': 'Maintenance Data',
'mock-ups': 'Mock-ups',
}
# Common standard organizations
STANDARD_PATTERNS = [
r'ASTM\s+[A-Z]\d+',
r'ANSI\s+[A-Z]?\d+',
r'ACI\s+\d+',
r'AISC\s+\d+',
r'AWS\s+[A-Z]\d+',
r'ASCE\s+\d+',
r'UL\s+\d+',
r'FM\s+\d+',
r'NFPA\s+\d+',
r'IBC\s+\d+',
]
def __init__(self):
self.sections: Dict[str, SpecSection] = {}
def extract_from_pdf(self, pdf_path: str) -> SpecExtractionResult:
"""Extract specification data from PDF."""
path = Path(pdf_path)
all_text = ""
page_count = 0
with pdfplumber.open(pdf_path) as pdf:
page_count = len(pdf.pages)
for page in pdf.pages:
text = page.extract_text() or ""
all_text += text + "\n\n"
# Parse sections
sections = self._parse_sections(all_text)
# Extract products
products = self._extract_products(sections)
# Extract submittals
submittals = self._extract_submittals(sections)
# Extract standards
standards = self._extract_standards(all_text)
return SpecExtractionResult(
document_name=path.name,
total_pages=page_count,
sections=sections,
products=products,
submittals=submittals,
standards_referenced=standards
)
def _parse_sections(self, text: str) -> List[SpecSection]:
"""Parse CSI sections from specification text."""
sections = []
lines = text.split('\n')
current_section = None
current_part = None
current_content = []
for line in lines:
line = line.strip()
if not line:
continue
# Check for section header
section_match = re.match(self.CSI_SECTION_PATTERN, line, re.IGNORECASE)
if section_match:
# Save previous section
if current_section:
sections.append(self._finalize_section(current_section, current_content))
current_section = {
'number': section_match.group(1).replace(' ', ''),
'title': section_match.group(2).strip(),
'parts': {}
}
current_content = []
current_part = None
continue
# Check for part header
part_match = re.match(self.PART_PATTERN, line, re.IGNORECASE)
if part_match and current_section:
part_num = part_match.group(1)
part_name = part_match.group(2).strip()
current_part = f"part{part_num}"
current_section['parts'][current_part] = {
'name': part_name,
'content': []
}
continue
# Add content to current part
if current_section and current_part:
current_section['parts'][current_part]['content'].append(line)
elif current_section:
current_content.append(line)
# Save last section
if current_section:
sections.append(self._finalize_section(current_section, current_content))
return sections
def _finalize_section(self, section_data: Dict, general_content: List[str]) -> SpecSection:
"""Finalize a section with parsed parts."""
parts = section_data.get('parts', {})
part1 = self._parse_part_content(parts.get('part1', {}).get('content', []))
part2 = self._parse_part_content(parts.get('part2', {}).get('content', []))
part3 = self._parse_part_content(parts.get('part3', {}).get('content', []))
return SpecSection(
number=section_data['number'],
title=section_data['title'],
part1_general=part1,
part2_products=part2,
part3_execution=part3,
raw_text='\n'.join(general_content)
)
def _parse_part_content(self, content: List[str]) -> Dict[str, Any]:
"""Parse part content into structured data."""
result = {
'articles': {},
'items': []
}
current_article = None
for line in content:
# Check for article header
article_match = re.match(self.ARTICLE_PATTERN, line)
if article_match:
current_article = article_match.group(1)
result['articles'][current_article] = {
'title': article_match.group(2),
'items': []
}
continue
# Add to current article or general items
if current_article and current_article in result['articles']:
result['articles'][current_article]['items'].append(line)
else:
result['items'].append(line)
return result
def _extract_products(self, sections: List[SpecSection]) -> List[ProductRequirement]:
"""Extract product requirements from Part 2."""
products = []
for section in sections:
part2 = section.part2_products
for article_num, article in part2.get('articles', {}).items():
if 'MANUFACTURERS' in article['title'].upper():
for item in article['items']:
# Extract manufacturer names
if item.strip().startswith(('A.', 'B.', 'C.', '1.', '2.', '3.')):
mfr_name = re.sub(r'^[A-Z\d]+\.\s*', '', item).strip()
products.append(ProductRequirement(
section=section.number,
manufacturer=mfr_name,
product_name='',
model='',
standards=[],
properties={}
))
elif 'MATERIALS' in article['title'].upper() or 'PRODUCTS' in article['title'].upper():
for item in article['items']:
# Extract material requirements
standards = self._extract_standards(item)
if standards:
products.append(ProductRequirement(
section=section.number,
manufacturer='',
product_name=item[:100],
model='',
standards=standards,
properties={}
))
return products
def _extract_submittals(self, sections: List[SpecSection]) -> List[SubmittalRequirement]:
"""Extract submittal requirements from Part 1."""
submittals = []
for section in sections:
part1 = section.part1_general
for article_num, article in part1.get('articles', {}).items():
if 'SUBMITTAL' in article['title'].upper():
for item in article['items']:
item_lower = item.lower()
for keyword, submittal_type in self.SUBMITTAL_TYPES.items():
if keyword in item_lower:
submittals.append(SubmittalRequirement(
section=section.number,
submittal_type=submittal_type,
description=item.strip(),
timing='Prior to fabrication',
copies=3
))
break
return submittals
def _extract_standards(self, text: str) -> List[str]:
"""Extract referenced standards from text."""
standards = []
for pattern in self.STANDARD_PATTERNS:
matches = re.findall(pattern, text, re.IGNORECASE)
standards.extend(matches)
return list(set(standards))
def generate_submittal_log(self, result: SpecExtractionResult) -> str:
"""Generate submittal log from extraction results."""
lines = ["# Submittal Log", ""]
lines.append(f"**Project Specs:** {result.document_name}")
lines.append(f"**Total Submittals:** {len(result.submittals)}")
lines.append("")
lines.append("| # | Section | Type | Description | Status |")
lines.append("|---|---------|------|-------------|--------|")
for i, sub in enumerate(result.submittals, 1):
desc = sub.description[:50] + "..." if len(sub.description) > 50 else sub.description
lines.append(f"| {i} | {sub.section} | {sub.submittal_type} | {desc} | Pending |")
return "\n".join(lines)
def generate_product_schedule(self, result: SpecExtractionResult) -> str:
"""Generate product schedule from extraction results."""
lines = ["# Product Schedule", ""]
# Group by section
by_section = {}
for prod in result.products:
if prod.section not in by_section:
by_section[prod.section] = []
Truncated for display — read the full file on GitHub.
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