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 pdf-constructiongit 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/pdf-construction)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/pdf-construction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/pdf-construction/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/pdf-construction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/pdf-construction.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 | $0.00031 | $0.01302 |
| Opus 5.5 | $0.00012 | $0.00521 |
| Sonnet 5.5 | $0.00006 | $0.00260 |
| Haiku 4.5 | $0.00003 | $0.00130 |
Grade A, and why
pdf-construction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- pdf-construction — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Processing for Construction
Overview
Adapted from Anthropic's PDF skill for construction document workflows.
Construction Use Cases
1. RFI Processing
Extract structured data from Request for Information documents.
from pypdf import PdfReader
import re
def extract_rfi_data(pdf_path: str) -> dict:
"""Extract RFI fields from PDF."""
reader = PdfReader(pdf_path)
text = ""
for page in reader.pages:
text += page.extract_text()
# Parse common RFI fields
rfi_data = {
'rfi_number': re.search(r'RFI\s*#?\s*(\d+)', text),
'subject': re.search(r'Subject:?\s*(.+?)(?:\n|$)', text),
'from': re.search(r'From:?\s*(.+?)(?:\n|$)', text),
'to': re.search(r'To:?\s*(.+?)(?:\n|$)', text),
'date': re.search(r'Date:?\s*(\d{1,2}[/-]\d{1,2}[/-]\d{2,4})', text),
'spec_section': re.search(r'Spec(?:ification)?\s*Section:?\s*(.+?)(?:\n|$)', text),
'drawing_ref': re.search(r'Drawing\s*(?:Ref)?:?\s*(.+?)(?:\n|$)', text),
}
return {k: v.group(1) if v else None for k, v in rfi_data.items()}
2. Submittal Package Creation
Merge multiple PDFs into organized submittal packages.
from pypdf import PdfWriter, PdfReader
from pathlib import Path
def create_submittal_package(
cover_sheet: str,
product_data: list,
shop_drawings: list,
output_path: str
) -> str:
"""Create organized submittal package."""
writer = PdfWriter()
# Add cover sheet
writer.append(cover_sheet)
# Add bookmarked sections
page_num = len(PdfReader(cover_sheet).pages)
# Product Data section
writer.add_outline_item("Product Data", page_num)
for pdf in product_data:
writer.append(pdf)
page_num += len(PdfReader(pdf).pages)
# Shop Drawings section
writer.add_outline_item("Shop Drawings", page_num)
for pdf in shop_drawings:
writer.append(pdf)
page_num += len(PdfReader(pdf).pages)
with open(output_path, "wb") as output:
writer.write(output)
return output_path
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 · 178 lines · 31 tokens per session scan A 7b92e220da21
pdf-construction 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 31 tokens to every session and 1,302 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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Use when you need full-text PDFs for a list of DOIs, such as a meta-analysis screening set. Batch-downloads open-access copies via Unpaywall, PMC, OpenAlex and Crossref, lists paywalled papers for manual access, and can convert PDFs to Markdown.