MateClaw is a pluggable agent runtime that coordinates AI agents, tools, conversations, memory, and workflows. It is designed for personal and multi-user AI assistants, including agents used through chat platforms. Catalogue add-ons provide skills for extending its agent workflows.
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 mateaix/mateclaw --skill pdfgit clone --depth 1 https://github.com/mateaix/mateclawWrote 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/mateaix/mateclaw/pdf)<a href="https://agentmods.dev/skills/mateaix/mateclaw/pdf"><img src="https://agentmods.dev/badge/skills/mateaix/mateclaw/pdf/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/mateaix/mateclaw/pdf"><img src="https://agentmods.dev/badge/skills/mateaix/mateclaw/pdf.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 | $0.00092 | $0.02541 |
| Opus 5.5 | $0.00037 | $0.01016 |
| Sonnet 5.5 | $0.00018 | $0.00508 |
| Haiku 4.5 | $0.00009 | $0.00254 |
Grade A, and why
pdf 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.
How it starts
The opening of the file, as written. The whole thing — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Important: All
scripts/paths are relative to this skill directory. Userun_skill_scripttool to execute scripts, or run with:cd {this_skill_dir} && python scripts/...
PDF Processing Guide
Prerequisites
- pypdf: core PDF reading and writing
- pdfplumber: text and table extraction
- reportlab: PDF creation
- pdftotext (poppler-utils): command-line text extraction
- pdftoppm (poppler-utils): PDF-to-image conversion
- qpdf: PDF manipulation (merge, split, rotate, decrypt)
Tool Selection Decision Table
Choose the right approach before starting:
| Input | Condition | Recommended Tool |
|---|---|---|
| URL | PDF accessible via URL | web_extract(url) — fastest, no download needed |
| Local file | Text-native PDF (generated by software) | pymupdf — ~25 MB install, instant extraction |
| Local file | Scanned/image-only PDF (no selectable text) | marker-pdf — OCR with layout preservation (~5 GB, needs GPU or CPU) |
| Local file | Form filling or page manipulation | pypdf / pdfplumber + form scripts |
| Local file | NLP editing or semantic search | nano-pdf — sentence-level operations |
URL-first rule: If the user provides a URL, always try URL extraction first before downloading.
Overview
This guide covers essential PDF processing operations using Python libraries and command-line tools.
URL-First Extraction
If the user provides a URL pointing to a PDF, extract it without downloading:
web_extract(url="https://example.com/report.pdf")
Fall back to download + local processing only if web_extract returns empty or errors.
Fast Extraction: pymupdf (fitz)
Best for: Text-native PDFs (digital, not scanned). Install: pip install pymupdf (~25 MB).
import fitz # pymupdf
doc = fitz.open("document.pdf")
print(f"Pages: {doc.page_count}")
# Extract all text (fast)
full_text = "\n".join(page.get_text() for page in doc)
# Extract with layout blocks (tables, columns)
for page in doc:
blocks = page.get_text("blocks") # (x0,y0,x1,y1,text,block_no,block_type)
for block in blocks:
print(block[4]) # text content
# Extract images
for page in doc:
for img in page.get_images():
xref = img[0]
base = doc.extract_image(xref)
with open(f"img_{xref}.{base['ext']}", "wb") as f:
f.write(base["image"])
What ships with it
11 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.
- forms.md 12 KB
- LICENSE.txt 1.4 KB
- reference.md 16 KB
- scripts/check_bounding_boxes.py 2.7 KB runs code
- scripts/check_fillable_fields.py 268 B runs code
- scripts/convert_pdf_to_images.py 1008 B runs code
- scripts/create_validation_image.py 1.2 KB runs code
- scripts/extract_form_field_info.py 4.2 KB runs code
- scripts/extract_form_structure.py 3.9 KB runs code
- scripts/fill_fillable_fields.py 3.7 KB runs code
- scripts/fill_pdf_form_with_annotations.py 3.2 KB runs code
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 · 364 lines · 92 tokens per session scan A f32633b77bc1
pdf is a skill published in the GitHub repository mateaix/mateclaw (1,164 stars, last pushed yesterday), licensed Apache-2.0. It adds 92 tokens to every session and 2,541 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
doc-reader
Read any common document/data file — PDF, Word (.docx), Excel (.xlsx/.xls), PowerPoint (.pptx), images (OCR), CSV/TSV, plain text, JSON/YAML/TOML, HTML/XML, and most source-code files. Use the readdocument tool.
oma-pdf
Extract PDF text, headings, tables, and images into Markdown using opendataloader-pdf. Use for PDF parsing, not PDF layout creation.
markdown-converter
Convert binary documents (PDF, DOCX, XLSX, PPTX, HTML, EPUB, images) to clean LLM-friendly Markdown using Microsoft's markitdown Python tool. Use when a user attaches a binary file and you need to read its contents.
pandic-office
Convert Markdown to PDF (or DOCX/EPUB/HTML) using the pandoc CLI. Use when asked to produce a PDF report, brief, summary, or any document where the input is Markdown and the output should be a polished, paginated file.