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 tokenbender/agent-guides --skill pdf-ocr-feedbackgit clone --depth 1 https://github.com/tokenbender/agent-guidesWrote 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/tokenbender/agent-guides/pdf-ocr-feedback)<a href="https://agentmods.dev/skills/tokenbender/agent-guides/pdf-ocr-feedback"><img src="https://agentmods.dev/badge/skills/tokenbender/agent-guides/pdf-ocr-feedback/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/tokenbender/agent-guides/pdf-ocr-feedback"><img src="https://agentmods.dev/badge/skills/tokenbender/agent-guides/pdf-ocr-feedback.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 | $0.00032 | $0.01606 |
| Opus 5.5 | $0.00013 | $0.00642 |
| Sonnet 5.5 | $0.00006 | $0.00321 |
| Haiku 4.5 | $0.00003 | $0.00161 |
Grade A, and why
pdf-ocr-feedback 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF OCR Feedback
Use this skill when transcribing PDF pages through a vision model and a single OCR pass is not reliable enough.
Objective
Produce page-faithful OCR with exact page boundaries, explicit uncertainty, and a practical target of at least 95/100 quality whenever the source allows it.
When to Use
Escalate to this workflow when any of the following are true:
- equations or mathematical notation matter,
- tables have nontrivial structure,
- the page is multi-column,
- the scan is noisy, low-resolution, or artifact-heavy,
- the document mixes languages, scripts, or handwriting,
- or a single OCR pass leaves meaningful uncertainty.
Output Contract
For every page:
- Preserve reading order.
- Capture all visible regions that matter: headers, footers, footnotes, captions, margin notes, table content, equation text, figure labels, and code blocks.
- Emit exact page delimiters:
===== PAGE N =====
<page text>
- Keep page order unchanged.
- Preserve equations, units, and table semantics.
- Never silently drop unknown symbols.
- If a tie cannot be resolved, mark the span explicitly as
[uncertain: "A" | "B"].
Pipeline Overview
For each page:
1. Pass-1 OCR
2. Self-evaluate on a 0-100 rubric
3. If score >= 95 and no red flags -> ACCEPT
4. Else run Maj@K escalation:
a. Generate K-1 additional independent passes
b. Vote at the smallest reliable unit
c. Re-score the merged result
d. If still weak, repair only flagged spans
5. Stop when accepted, capped, or no longer improving
Phase 1: Initial Transcription
For the first pass on every page:
- Transcribe the full page faithfully.
- Preserve top-to-bottom, left-to-right reading order. For multi-column pages, process column-by-column.
- Do not skip difficult regions; capture them or mark them uncertain.
- Keep formatting structure when it carries meaning, such as headings, lists, table rows, and code blocks.
Phase 2: Self-Evaluation
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 · 196 lines · 32 tokens per session scan A f6faf9c27c99
pdf-ocr-feedback is a skill published in the GitHub repository tokenbender/agent-guides (367 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,606 once invoked, about $0.0001 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.
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