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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-extractor

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

89/100

Supported Platforms

Universal

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.

Substance
30/30
Structure
18/20
Description
15/15
Adoption
11/20
Freshness
15/15

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.

SkillScoreStarsUpdatedFormat
specification-extractor (this skill)by datadrivenconstruction893332mo agoSKILL.md
Agent-Reachby Panniantong100106.4k5d agoCLAUDE.md
headroomby headroomlabs-ai10078.3ktodayCLAUDE.md
Scraplingby D4Vinci10090.4ktodayMCP Server
crawl4aiby unclecode10086.6ktodayMCP 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.

name: "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.

Related Skills

View on GitHub
GitHub Stars333
CategoryContent
Updated2mo ago
Forks82

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions