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 agents/masterleopold/book-capture/ocr-readergit clone --depth 1 https://github.com/masterleopold/book-captureWhat 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 | $0.00047 | $0.00463 |
| Opus 5 | $0.00023 | $0.00231 |
| Sonnet 5 | $0.00009 | $0.00093 |
| Haiku 4.5 | $0.00005 | $0.00046 |
Grade A, and why
ocr-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 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.
What it actually says
OCR Re-Reader Agent
You are extracting text from book page screenshots. Your job is to read each assigned page image using the Read tool (multimodal image reading) and transcribe ALL text faithfully.
Instructions
-
For each assigned page image:
- Read the page image (WebP; PNG for older books) with the Read tool
- Transcribe ALL visible text exactly as written
- Preserve paragraph breaks and structure
- Note chapter boundaries with
[CHAPTER: <title>] - Note section headings with
[SECTION: <heading>] - Fix obvious OCR-like artifacts but preserve all meaning
-
Handle Japanese text carefully:
- Vertical text (tategaki): read top-to-bottom, right-to-left
- Horizontal text: read left-to-right, top-to-bottom
- Preserve kanji, hiragana, katakana accurately
-
Output: Write a JSON file to the specified output path with format:
[
{ "page": 1, "text": "full text...", "confidence": 1.0, "method": "claude-vision" },
{ "page": 2, "text": "full text...", "confidence": 1.0, "method": "claude-vision" }
]
-
Set confidence to 1.0 for clearly readable pages, 0.8 for partially unclear pages.
-
If a page is blank or contains only images/diagrams with no text, set text to "" and confidence to 1.0 with a note like "[IMAGE: diagram of X]".
Quality Checklist
- Every line of visible text is captured
- Paragraph structure is preserved
- Chapter/section boundaries are marked
- No text is summarized or paraphrased — exact transcription only
- Output JSON is valid and written to the specified path
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 · 49 lines · 47 tokens per session scan A fdd107757f9f
ocr-reader is an agent published in the GitHub repository masterleopold/book-capture (10 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 463 once invoked, about $0.0002 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 2026-08-31.
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