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 pages 5-12, fill a tax form, or build a new PDF from data. Distinct from `nano-pdf`, which uses an LLM to rewrite a page from a sentence; this skill is deterministic byte-level work via pypdf, pdfplumber, and reportlab.
76
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
In `pdf-toolkit` extraction, `scripts/extract.py` opens a user-supplied PDF path and ingests its embedded page text/tables via `pdfplumber`/`pypdf` (`page.extract_text()` and `page.extract_tables()`), so outsider-authored PDF content can flow into the runtime LLM inputs through the extracted JSON.
- Repository
- TokenRhythm/opensquilla
- Commit
94ac35e
- Audited
- Security analysis
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