mcpbeat Sign in

Specification Extractor Agent Skill

Extract structured data from construction specifications. Parse CSI sections, requirements, submittals, and product data from spec documents.

4k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
264
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill specification-extractor

The instruction itself

6 sections, as written by the author

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] = []
            by_section[prod.section].append(prod)

        for section, products in sorted(by_section.items()):
            lines.append(f"## Section {section}")
            lines.append("")

            for prod in products:
                if prod.manufacturer:
                    lines.append(f"- **Manufacturer:** {prod.manufacturer}")
                if prod.product_name:
                    lines.append(f"- **Product:** {prod.product_name}")
                if prod.standards:
                    lines.append(f"- **Standards:** {', '.join(prod.standards)}")
                lines.append("")

        return "\n".join(lines)

    def generate_report(self, result: SpecExtractionResult) -> str:
        """Generate comprehensive extraction report."""
        lines = ["# Specification Extraction Report", ""]
        lines.append(f"**Document:** {result.document_name}")
        lines.append(f"**Pages:** {result.total_pages}")
        lines.append(f"**Sections Found:** {len(result.sections)}")
        lines.append("")

        # Sections summary
        lines.append("## Sections Extracted")
        for section in result.sections:
            lines.append(f"- **{section.number}** - {section.title}")
        lines.append("")

        # Standards
        if result.standards_referenced:
            lines.append("## Standards Referenced")
            for std in sorted(set(result.standards_referenced)):
                lines.append(f"- {std}")
            lines.append("")

        # Submittals summary
        lines.append("## Submittals Required")
        lines.append(f"Total: {len(result.submittals)}")
        by_type = {}
        for sub in result.submittals:
            by_type[sub.submittal_type] = by_type.get(sub.submittal_type, 0) + 1
        for t, count in sorted(by_type.items()):
            lines.append(f"- {t}: {count}")
        lines.append("")

        # Products summary
        lines.append("## Products/Manufacturers")
        lines.append(f"Total: {len(result.products)}")

        return "\n".join(lines)

Quick Start

# Initialize extractor
extractor = SpecificationExtractor()

# Extract from PDF
result = extractor.extract_from_pdf("Project_Specifications.pdf")

print(f"Found {len(result.sections)} sections")
print(f"Found {len(result.submittals)} submittals")
print(f"Found {len(result.products)} product requirements")

# Generate submittal log
submittal_log = extractor.generate_submittal_log(result)
print(submittal_log)

# Generate product schedule
product_schedule = extractor.generate_product_schedule(result)
print(product_schedule)

# Full report
report = extractor.generate_report(result)
print(report)

Dependencies

pip install pdfplumber

Other skills for the same job

different authors, same section of the catalogue
DOCX
by anthropics
vendor ×16

Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks

7k tokens
PDF
by anthropics
vendor ×16

Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.

13k tokens scripts
PPTX
by JayZeeDesign
×15

Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks

308k tokens scripts
Canvas Design
by anthropics
vendor ×13

Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.

1388k tokens
PDF
by anthropics
vendor ×10

Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.

15k tokens scripts
DOCX
by w95
×6

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

5k tokens
PPTX
by w95
×4

Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.

2k tokens
Obsidian Markdown
by ZhanlinCui
×3

Create and edit Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties, and other Obsidian-specific syntax. Use when working with .md files in Obsidian, or when the user mentions wikilinks, callouts, frontmatter, tags, embeds, or Obsidian notes.

3k tokens

How to use it

Copy the folder

Take datadrivenconstruction/specification-extractor from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.