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 specification-extractorgit 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/specification-extractor)<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/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/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>- 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 | $0.00029 | $0.03034 |
| Opus 5.5 | $0.00012 | $0.01214 |
| Sonnet 5.5 | $0.00006 | $0.00607 |
| Haiku 4.5 | $0.00003 | $0.00303 |
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 1mo 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:
- specification-extractor — 100% identical, 0 lines differ
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)
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.
- 1mo ago First seen · 420 lines · 29 tokens per session scan A fd33b6424955
specification-extractor is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (374 stars, last pushed 8d ago), licensed MIT. It adds 29 tokens to every session and 3,034 once invoked, about $0.0001 per session on Opus 5.5. 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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