pdf-ocr-feedback

pdf-ocr-feedback is a skill for Claude Code, Codex from tokenbender/agent-guides. It costs 32 tokens per session (1,606 once invoked), scanned A, original, Apache-2.0.

A process for improving OCR, or text recognition from PDF pages, when a single pass may contain important errors. It compares multiple readings and repairs uncertain parts while preserving page structure.

In plain words
What is it for?
It helps produce page-by-page transcriptions with reading order, page markers, preserved symbols and tables, quality scores, and explicit notes for unresolved text.
Why use it?
It reduces mistakes in equations, tables, columns, footnotes, figures, mixed languages, and poor-quality scans. It also records uncertainty instead of silently guessing.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/tokenbender/agent-guides/pdf-ocr-feedback
Any agent
npx skills add tokenbender/agent-guides --skill pdf-ocr-feedback
Clone the repo
git clone --depth 1 https://github.com/tokenbender/agent-guides

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for pdf-ocr-feedback

README.md
[![agentmods](https://agentmods.dev/badge/skills/tokenbender/agent-guides/pdf-ocr-feedback.svg)](https://agentmods.dev/skills/tokenbender/agent-guides/pdf-ocr-feedback)
Your own site
<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="Measured on agentmods" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,606 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00032 $0.01606
Opus 5 $0.00016 $0.00803
Sonnet 5 $0.00006 $0.00321
Haiku 4.5 $0.00003 $0.00161

Measured 4d ago against content hash f6faf9c27c99, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

claude-skills/pdf-ocr-feedback/SKILL.md · 196 lines

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:

  1. Preserve reading order.
  2. Capture all visible regions that matter: headers, footers, footnotes, captions, margin notes, table content, equation text, figure labels, and code blocks.
  3. Emit exact page delimiters:
===== PAGE N =====
<page text>
  1. Keep page order unchanged.
  2. Preserve equations, units, and table semantics.
  3. Never silently drop unknown symbols.
  4. 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:

  1. Transcribe the full page faithfully.
  2. Preserve top-to-bottom, left-to-right reading order. For multi-column pages, process column-by-column.
  3. Do not skip difficult regions; capture them or mark them uncertain.
  4. Keep formatting structure when it carries meaning, such as headings, lists, table rows, and code blocks.

Phase 2: Self-Evaluation

Read the full file on GitHub · 196 lines

Changes

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.

  1. 4d ago First seen · 196 lines · 32 tokens per session scan A f6faf9c27c99

Subscribe to this mod's changes

pdf-ocr-feedback is a skill published in the GitHub repository tokenbender/agent-guides (368 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,606 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 2026-08-30.

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