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 agentmods add skills/starrycod/cogitum/ocr-and-documentsnpx skills add StarryCod/cogitum --skill ocr-and-documentsgit clone --depth 1 https://github.com/StarryCod/cogitumWrote 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/starrycod/cogitum/ocr-and-documents)<a href="https://agentmods.dev/skills/starrycod/cogitum/ocr-and-documents"><img src="https://agentmods.dev/badge/skills/starrycod/cogitum/ocr-and-documents.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.01438 |
| Opus 5 | $0.00010 | $0.00719 |
| Sonnet 5 | $0.00004 | $0.00288 |
| Haiku 4.5 | $0.00002 | $0.00144 |
Grade A, and why
ocr-and-documents 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 2d 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.
This is a copy
89% identical to ocr-and-documents — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF & Document Extraction
For DOCX: use python-docx (parses actual document structure, far better than OCR).
For PPTX: see the powerpoint skill (uses python-pptx with full slide/notes support).
This skill covers PDFs and scanned documents.
Step 1: Remote URL Available?
If the document has a URL, always try web_extract first:
web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
web_extract(urls=["https://example.com/report.pdf"])
This handles PDF-to-markdown conversion via Firecrawl with no local dependencies.
Only use local extraction when: the file is local, web_extract fails, or you need batch processing.
Step 2: Choose Local Extractor
| Feature | pymupdf (~25MB) | marker-pdf (~3-5GB) |
|---|---|---|
| Text-based PDF | ✅ | ✅ |
| Scanned PDF (OCR) | ❌ | ✅ (90+ languages) |
| Tables | ✅ (basic) | ✅ (high accuracy) |
| Equations / LaTeX | ❌ | ✅ |
| Code blocks | ❌ | ✅ |
| Forms | ❌ | ✅ |
| Headers/footers removal | ❌ | ✅ |
| Reading order detection | ❌ | ✅ |
| Images extraction | ✅ (embedded) | ✅ (with context) |
| Images → text (OCR) | ❌ | ✅ |
| EPUB | ✅ | ✅ |
| Markdown output | ✅ (via pymupdf4llm) | ✅ (native, higher quality) |
| Install size | ~25MB | ~3-5GB (PyTorch + models) |
| Speed | Instant | ~1-14s/page (CPU), ~0.2s/page (GPU) |
Decision: Use pymupdf unless you need OCR, equations, forms, or complex layout analysis.
If the user needs marker capabilities but the system lacks ~5GB free disk:
"This document needs OCR/advanced extraction (marker-pdf), which requires ~5GB for PyTorch and models. Your system has [X]GB free. Options: free up space, provide a URL so I can use web_extract, or I can try pymupdf which works for text-based PDFs but not scanned documents or equations."
pymupdf (lightweight)
pip install pymupdf pymupdf4llm
Via helper script:
python scripts/extract_pymupdf.py document.pdf # Plain text
python scripts/extract_pymupdf.py document.pdf --markdown # Markdown
python scripts/extract_pymupdf.py document.pdf --tables # Tables
python scripts/extract_pymupdf.py document.pdf --images out/ # Extract images
python scripts/extract_pymupdf.py document.pdf --metadata # Title, author, pages
python scripts/extract_pymupdf.py document.pdf --pages 0-4 # Specific pages
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.
- 2d ago First seen · 173 lines · 21 tokens per session scan A e3be9df59a01
ocr-and-documents is a skill published in the GitHub repository StarryCod/cogitum (11 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 1,438 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to ocr-and-documents, differing in 20 lines, and is treated as a copy.
Other skills, from other repositories
Nano PDF
Extract, inspect, and summarize PDFs quickly with lightweight tooling before escalating to heavier OCR or layout workflows.
OCR and Documents
Recover text from scanned or image-heavy documents before attempting structured analysis or downstream writing tasks.
nano-pdf
Edits PDF files using natural-language instructions via the nano-pdf CLI. Supports modifying text, changing titles, fixing typos, and updating content on specific pages. Use when the user wants to edit a PDF, modify PDF content, update PDF text, fix a typo in a PDF, change a PDF title, or rewrite part of a PDF page.
pdf-toolkit
Structured .pdf operations: extract text/tables, merge pages from multiple PDFs, split a PDF by page ranges, fill PDF form fields, and generate fresh PDFs from JSON. Trigger when the user wants programmatic PDF work without natural-language rewriting — examples: pull tables from a report, combine three PDFs, extract…
nano-pdf
Edit PDFs with natural-language instructions using the nano-pdf CLI.
liteparse
Use this skill when the user asks to parse, perform multi-format document conversion or spatially extract text from an unstructured file (PDF, DOCX, PPTX, XLSX, images, etc.) locally without cloud dependencies.