specification-extractor

specification-extractor is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 29 tokens per session (3,034 once invoked), scanned A, original, MIT.

A document-reading tool for construction specifications. It finds structured details such as CSI sections, product requirements, standards, and required submittals; CSI MasterFormat is a system for organizing construction work specifications.

In plain words
What is it for?
Use it to extract specification sections, product and manufacturer details, applicable standards, properties, and submittal requirements from documents.
Why use it?
It removes the need to search long specification documents manually. This helps estimators, buyers, and project teams find requirements more consistently.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to extract specification sections, product and manufacturer details, applicable standards, properties, and submittal requirements from documents.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/specification-extractor
Install

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.

Any agent
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill specification-extractor
Clone the repo
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for specification-extractor

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/specification-extractor/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/specification-extractor)
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agentmods 80×15 button for specification-extractor

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<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/specification-extractor"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/specification-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,034 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.03034
Opus 5 $0.00015 $0.01517
Sonnet 5 $0.00006 $0.00607
Haiku 4.5 $0.00003 $0.00303

Measured 7d ago against content hash fd33b6424955, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

specification-extractor 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

2_DDC_Book/2.4-PDF-CAD-to-Data/specification-extractor/SKILL.md · 420 lines

How it starts

The opening of the file, as written. The whole thing — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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)

Read the full file on GitHub · 420 lines

Files

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.

Changes

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.

  1. 7d ago First seen · 420 lines · 29 tokens per session scan A fd33b6424955

Subscribe to this mod's changes

specification-extractor 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 29 tokens to every session and 3,034 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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