Post-OCR Text Cleanup for Research Corpora Agent Skill

Clean post-OCR text: correction, QA, multilingual handling, provenance.

Context cost
7k tokensestimated from the bundle, loaded when it triggers
Bundle
2 files · 27.8 kBtext throughout, nothing executable
Licence
Source-availablethe author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Clean post-OCR text: correction, QA, multilingual handling, provenance.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

data scienceresearch
Filed under

Research

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md18.7 kB · preview
--- name: post-ocr-cleanup argument-hint: "[describe your OCR output, languages, and error patterns]" ---
7# Post-OCR Text Cleanup for Research Corpora
8
9## Instructions
10
11### 1. Cleanup Strategy Selection
12
13- **Characterize the error-generating DGP before selecting a method.** Document source language(s), era, typeface family (Fraktur, Antiqua, typewritten, handwritten), scan DPI, OCR engine, and domain jargon. Each parameter constrains which corrections are plausible and which risk introducing semantic drift.
14- **Choose between LLM correction, rule-based fixes, or a hybrid pipeline based on error type.** LLM correction excels at context-dependent errors (wrong but plausible characters, broken words, missing diacritics). Rule-based fixes handle deterministic patterns (control characters, Unicode normalization, repetition artifacts, whitespace) with zero risk of content alteration. Use rule-based fixes unconditionally for these categories.
15- **Default to the hybrid approach for research corpora.** Run LLM correction first on all pages, then apply deterministic rule fixes on top. This order matters: LLM correction may introduce formatting artifacts that rule fixes clean up, while the reverse order wastes rule-fix effort on text the LLM will rewrite (Machidon & Machidon 2025).
16- **Pilot-test LLM correction per language before corpus-wide deployment.** LLM post-correction effectiveness is highly language-dependent: English achieves 7-58% CER reduction across open models, while Finnish shows negative or near-zero improvement across the same model set (Kanerva et al. 2025). Never assume cross-language transferability.
17- **Consider whether correction is needed at all.** Define the quality threshold before choosing a strategy. The Hill & Hengchen 70-80% quality band (reported in van Strien et al. 2020) marks the critical threshold below which most downstream NLP tasks perform poorly; above 80% quality many tasks (e.g., topic modeling) tolerate residual noise. If the downstream analysis sits comfortably above this band, the risk of correction-introduced errors may outweigh the benefit.
18
19### 2. LLM-Based Correction
20
21- **For most pages, use a small text-only model.** The correction input is already text; image understanding is not needed for well-OCR'd pages. A 7-13B parameter model with 4-bit quantization fits in ~4-20GB VRAM and runs on a single GPU. Larger fp16 models (e.g., Llama-3.1-70B at fp16 yielding ~42% CER reduction vs ~39% at 4-bit) gain 2.5-4.7pp but require roughly 3x the memory (132GB vs 43GB) and often a second GPU (Kanerva et al. 2025).
22- **For severely degraded pages, use multimodal correction.** Feeding both the original page image and the OCR text to a correction model can achieve below 1% CER on degraded documents, but doubles GPU cost (Greif et al. 2025). Reserve this for flagged pages, not routine processing.
23- **Write tight correction prompts.** Instruct the model to "fix clear OCR mistakes only: wrong characters, broken words, garbled punctuation, repetition artifacts. Do not translate, modernize, or add anything. Output the corrected text only." Loose prompts invite hallucination.
24- **Supply socio-cultural context in the prompt.** Including document era, publication type, language register, and genre (e.g., "The text is from an English newspaper in the 1800s") meaningfully reduces CER beyond generic correction prompts — the top-performing CLOCR-C configuration achieved over 60% CER reduction on the NCSE dataset using a modular prompt that combines expert framing, recovery instructions, publication context, text-type context, and anti-overgeneration instructions (Bourne 2024). Misleading or mismatched context degrades performance, so use the real document metadata.
25- **Add language-specific instructions.** For Polish, explicitly mention diacritics restoration (ą, ć, ę, ł, ń, ó, ś, ź, ż). For Korean, mention hangul integrity and hanja preservation. The correction model needs to know which character set to favor.
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md2,568 words
Files2
LicenceSource-available
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈50
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
6,900
on trigger
The instruction body and 1 supporting file, read only when the skill fires.
3.5%
of a 200k window
Ten skills this size would take about 35% of the window before you open a file.
050k100k150k200k context window

7k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

2 files, 27.8 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md18.7 kB
  • reference/prompt-templates-and-schema.md9.1 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 2 files you can review in full before installing. The Source-available licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Post-OCR Text Cleanup for Research Corpora · 7k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/post-ocr-text-cleanup-for-research-corpora/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/post-ocr-text-cleanup-for-research-corpora" ] && { [ ! -e .claude/skills/post-ocr-text-cleanup-for-research-corpora ] || { mv .claude/skills/post-ocr-text-cleanup-for-research-corpora "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/post-ocr-text-cleanup-for-research-corpora" .claude/skills/ && echo "Installed .claude/skills/post-ocr-text-cleanup-for-research-corpora"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add brycewang-stanford/post-ocr-text-cleanup-for-research-corpora

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/post-ocr-text-cleanup-for-research-corpora/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/post-ocr-text-cleanup-for-research-corpora/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/post-ocr-text-cleanup-for-research-corpora" ] && { [ ! -e ~/.claude/skills/post-ocr-text-cleanup-for-research-corpora ] || { mv ~/.claude/skills/post-ocr-text-cleanup-for-research-corpora "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/post-ocr-text-cleanup-for-research-corpora" ~/.claude/skills/ && echo "Installed ~/.claude/skills/post-ocr-text-cleanup-for-research-corpora" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/post-ocr-text-cleanup-for-research-corpora/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. It appears as post-ocr-cleanup in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, post-ocr-text-cleanup-for-research-corpora.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencebrycewang-stanford/post-ocr-text-cleanup-for-research-corpora

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

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