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 JansenAnalytics/claudex --skill doc-readergit clone --depth 1 https://github.com/JansenAnalytics/claudexWrote 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/jansenanalytics/claudex/doc-reader)<a href="https://agentmods.dev/skills/jansenanalytics/claudex/doc-reader"><img src="https://agentmods.dev/badge/skills/jansenanalytics/claudex/doc-reader/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/jansenanalytics/claudex/doc-reader"><img src="https://agentmods.dev/badge/skills/jansenanalytics/claudex/doc-reader.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00051 | $0.02657 |
| Opus 5.5 | $0.00020 | $0.01063 |
| Sonnet 5.5 | $0.00010 | $0.00531 |
| Haiku 4.5 | $0.00005 | $0.00266 |
Grade B, and why
doc-reader scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt-get install tesseract-ocr-<lang> How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Doc Reader — Document Intelligence
Extract text, tables, and insights from any document format. Handles native text PDFs, scanned documents (OCR), Word files, spreadsheets, images, and HTML.
Quick Reference
SCRIPTS=${CLAUDE_SKILLS_DIR:-$HOME/.claude-agent/.claude/skills}/doc-reader/scripts
# Extract text from any document
python3 $SCRIPTS/extract.py document.pdf
# Extract tables only
python3 $SCRIPTS/extract.py document.pdf --mode tables
# Full analysis (text + tables + metadata)
python3 $SCRIPTS/extract.py document.pdf --mode summary
# OCR scanned document
python3 $SCRIPTS/extract.py scanned.pdf --mode ocr --ocr-lang eng+nor
# Analyze document (classify, extract figures, structure)
python3 $SCRIPTS/analyze.py document.pdf --extract-figures
# Convert between formats
python3 $SCRIPTS/convert.py report.pdf report.md
python3 $SCRIPTS/convert.py data.xlsx data.csv
Scripts
1. extract.py — Universal Text & Table Extraction
The core extraction engine. Supports PDF, DOCX, XLSX, CSV, images, HTML, and plain text.
Extraction Modes
| Mode | Description |
|---|---|
text |
Full text extraction (default). Auto-falls back to OCR for scanned pages |
tables |
Table extraction only (as markdown, CSV, or JSON) |
meta |
Document metadata (author, dates, page count, file size) |
summary |
Text + tables + metadata combined |
ocr |
Force OCR on all pages (for fully scanned documents) |
layout |
Layout-preserving text extraction via pdftotext (columns, spacing) |
images |
Extract embedded images from PDF |
Usage
# Basic text extraction
python3 extract.py report.pdf
python3 extract.py contract.docx
python3 extract.py financials.xlsx
# Specific pages
python3 extract.py annual-report.pdf --pages 1-5,8,12-15
# Table extraction as JSON
python3 extract.py financials.pdf --mode tables --table-format json
# OCR with Norwegian + English
python3 extract.py scan.pdf --mode ocr --ocr-lang eng+nor
# Full summary to file
python3 extract.py report.pdf --mode summary --output /tmp/report-summary.txt
# JSON output (for programmatic use)
python3 extract.py report.pdf --mode summary --json --output /tmp/report.json
# Excel: specific sheet
python3 extract.py data.xlsx --sheet "Revenue" --mode tables
# Layout mode (preserves columns in multi-column PDFs)
python3 extract.py newspaper.pdf --mode layout
# Limit page count
python3 extract.py huge-doc.pdf --max-pages 20
What ships with it
3 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 · 283 lines · 51 tokens per session scan B 6925749b9cce
doc-reader is a skill published in the GitHub repository JansenAnalytics/claudex (5 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 2,657 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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kreuzberg
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format-specific-extraction
ZIP archive → Security validation → XML parsing → Text + tables + metadata.
xberg-extracting-tables
Use when extracting tabular data from PDFs, spreadsheets, or images. Covers layout-aware table detection, table model selection, output formats (markdown / JSON cells), and known limits.
xberg-extracting-with-ocr
Use when extracting text from scanned PDFs, photographed pages, or images that have no embedded text layer. Covers OCR backends, language packs, force-OCR, and performance tuning.
document-processing
Use when the deliverable is a document's bytes or its literal content — text/tables out of PDFs, AcroForm fill and flatten, page merge/split, PDF/DOCX from templates, OCR of image-only scans. NOT schema-typed fields pulled from text (that is structured-extraction), NOT signature routing (e-signature) or spreadsheet…
document-processing-expert
Read, generate and modify office documents and PDFs from code: PDF extraction and forms, Word documents, Excel workbooks and PowerPoint decks. Use when the user mentions PDF, DOCX, XLSX, PPTX, Word, Excel, PowerPoint or spreadsheets, wants data extracted from documents, needs a report or invoice generated as a file…