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/rightnow-ai/openfang/pdf-readernpx skills add RightNow-AI/openfang --skill pdf-readergit clone --depth 1 https://github.com/RightNow-AI/openfangWrote 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/rightnow-ai/openfang/pdf-reader)<a href="https://agentmods.dev/skills/rightnow-ai/openfang/pdf-reader"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/pdf-reader.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 | $0.00009 | $0.00555 |
| Opus 5 | $0.00005 | $0.00278 |
| Sonnet 5 | $0.00002 | $0.00111 |
| Haiku 4.5 | $0.00001 | $0.00056 |
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 4d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- pdf-reader — 100% identical, 0 lines differ
- pdf-reader — 92% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 52 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.
- 4d ago First seen · 52 lines · 9 tokens per session scan A 72e73231efbc
pdf-reader is a skill published in the GitHub repository RightNow-AI/openfang (18,162 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 9 tokens to every session and 555 once invoked, about $0.0000 per session on Opus 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
officecli-commonly-templates
Use this skill when producing a polished, Commonly-branded deliverable (.docx brief / memo, .xlsx data matrix, .pptx deck) and you do not have specific brand guidance from the user. Trigger on: 'write me a brief', 'one-pager', 'memo', 'data sheet', 'status matrix', 'short deck', 'summary deck', 'closing slide', 'final…
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.
Manipulate PDF files — extract text, count pages, render thumbnails, merge or split documents. Use for PDF-specific operations that don't fit markdown-converter (general read) or pandic-office (write from markdown).
drawio-skill
Use when user requests diagrams, flowcharts, architecture charts, or visualizations. Also use proactively when explaining systems with 3+ components, complex data flows, or relationships that benefit from visual representation. Generates .drawio XML files and exports to PNG/SVG/PDF locally using the native draw.io…
feishu-create-doc
创建飞书云文档。从 Lark-flavored Markdown 内容创建新的飞书云文档，支持指定创建位置（文件夹/知识库/知识空间）。.