pdf-construction

pdf-construction is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 31 tokens per session (1,302 once invoked), scanned A, original, MIT.

A tool for working with construction PDF documents such as RFIs, submittals, specifications, and drawing packages. RFI means Request for Information, a formal project question that needs a recorded answer.

In plain words
What is it for?
Use it to extract RFI fields, create submittal packages, process specifications, and merge or fill construction PDF documents.
Why use it?
It helps extract structured information from PDFs and combine documents into organised packages. This reduces manual copying and document-management work.

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 RFI fields, create submittal packages, process specifications, and merge or fill construction PDF documents.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/pdf-construction/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/pdf-construction)
Your own site
<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.

agentmods 80×15 button for pdf-construction

Your own site · 80×15
<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>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,302 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 $0.00031 $0.01302
Opus 5 $0.00015 $0.00651
Sonnet 5 $0.00006 $0.00260
Haiku 4.5 $0.00003 $0.00130

Measured 9d ago against content hash 7b92e220da21, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 9d 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:

4_DDC_Curated/Document-Generation/pdf-construction/SKILL.md · 178 lines

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

Read the full file on GitHub · 178 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. 9d ago First seen · 178 lines · 31 tokens per session scan A 7b92e220da21

Subscribe to this mod's changes

pdf-construction is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (312 stars, last pushed 21d ago), licensed MIT. It adds 31 tokens to every session and 1,302 once invoked, about $0.0002 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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