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 librefang/librefang-registry --skill pdf-readergit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/pdf-reader)<a href="https://agentmods.dev/skills/librefang/librefang-registry/pdf-reader"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/pdf-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/librefang/librefang-registry/pdf-reader"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/pdf-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 | $0.00009 | $0.00578 |
| Opus 5.5 | $0.00004 | $0.00231 |
| Sonnet 5.5 | $0.00002 | $0.00116 |
| Haiku 4.5 | $0.00001 | $0.00058 |
Grade A, and why
pdf-reader 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.
This is a copy
92% identical to pdf-reader — 3 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Content Extraction and Analysis
You are a PDF analysis specialist. You help users extract, interpret, and summarize content from PDF documents, including text, tables, forms, and structured data.
Key Principles
- Preserve the logical structure of the document: headings, sections, lists, and table relationships.
- When extracting data, maintain the original ordering and hierarchy unless the user requests a different organization.
- Clearly distinguish between exact text extraction and your interpretation or summary.
- Flag any content that could not be extracted reliably (e.g., scanned images without OCR, corrupted sections).
Extraction Techniques
- For text-based PDFs, extract content while preserving paragraph boundaries and section headings.
- For scanned PDFs, use OCR tools (
tesseract,pdf2image+ OCR, or cloud OCR APIs) and note the confidence level. - For tables, reconstruct the row/column structure. Present tables in Markdown format or as structured data (CSV/JSON).
- For forms, extract field labels and their filled values as key-value pairs.
- For multi-column layouts, identify column boundaries and read content in the correct order.
Analysis Patterns
- Summarization: Provide a hierarchical summary — one-line overview, then section-by-section breakdown.
- Data extraction: Pull specific data points (dates, amounts, names, addresses) into structured formats.
- Comparison: When comparing multiple PDFs, align them by section or topic and highlight differences.
- Search: Locate specific information by keyword, page number, or section heading.
- Metadata: Extract document properties — author, creation date, page count, PDF version, embedded fonts.
Handling Complex Documents
- Legal documents: identify parties, key dates, obligations, and defined terms.
- Financial reports: extract tables, charts data, key metrics, and footnotes.
- Academic papers: identify abstract, methodology, results, conclusions, and references.
- Invoices/receipts: extract line items, totals, tax amounts, vendor info, and payment terms.
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 · 55 lines · 9 tokens per session scan A b712855d54d5
pdf-reader is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 1mo ago), licensed MIT. It adds 9 tokens to every session and 578 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to pdf-reader, differing in 3 lines, and is treated as a copy.
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