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donut

Use Donut for OCR-free document understanding, checkpoint inference, fine-tuning, evaluation, and SynthDoG synthetic document generation.

Skill by VectorSpaceLab

IndexedMIT★ 341
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Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/vectorspacelab-donut

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Use Donut for OCR-free document understanding, checkpoint inference, fine-tuning, evaluation, and SynthDoG synthetic document generation.

Use cases: documents, ml

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found
Runs as
Python package

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/donut

What's inside

Skill name
donut
Description
Use Donut for OCR-free document understanding, checkpoint inference, fine-tuning, evaluation, and SynthDoG synthetic document generation.
License
MIT
Metadata
disco-role: operating
Instructions
53 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (44)

  • SKILL.md
  • api-reference.md · references/
  • repo-provenance.md · references/
  • repo-routing-metadata.json · references/
  • troubleshooting.md · references/
  • workflow-map.md · references/
  • runtime_smoke.py · scripts/
  • SKILL.md · sub-skills/inference/
  • demo-and-prediction.md · sub-skills/inference/references/
  • troubleshooting.md · sub-skills/inference/references/
  • launch_demo.py · sub-skills/inference/scripts/
  • run_inference.py · sub-skills/inference/scripts/
  • SKILL.md · sub-skills/synthdog/
  • config_en.yaml · sub-skills/synthdog/references/configs/
  • config_ja.yaml · sub-skills/synthdog/references/configs/
  • config_ko.yaml · sub-skills/synthdog/references/configs/
  • config_zh.yaml · sub-skills/synthdog/references/configs/
  • configuration.md · sub-skills/synthdog/references/
  • resource-layout.md · sub-skills/synthdog/references/
  • troubleshooting.md · sub-skills/synthdog/references/
  • workflows.md · sub-skills/synthdog/references/
  • __init__.py · sub-skills/synthdog/scripts/elements/
  • background.py · sub-skills/synthdog/scripts/elements/
  • content.py · sub-skills/synthdog/scripts/elements/
  • document.py · sub-skills/synthdog/scripts/elements/
  • paper.py · sub-skills/synthdog/scripts/elements/
  • textbox.py · sub-skills/synthdog/scripts/elements/
  • __init__.py · sub-skills/synthdog/scripts/layouts/
  • grid.py · sub-skills/synthdog/scripts/layouts/
  • grid_stack.py · sub-skills/synthdog/scripts/layouts/
  • render_config.py · sub-skills/synthdog/scripts/
  • template.py · sub-skills/synthdog/scripts/
  • SKILL.md · sub-skills/training/
  • train_cord.yaml · sub-skills/training/references/configs/
  • train_docvqa.yaml · sub-skills/training/references/configs/
  • train_rvlcdip.yaml · sub-skills/training/references/configs/
  • train_zhtrainticket.yaml · sub-skills/training/references/configs/
  • configuration.md · sub-skills/training/references/
  • troubleshooting.md · sub-skills/training/references/
  • workflows.md · sub-skills/training/references/
  • check_training_config.py · sub-skills/training/scripts/
  • evaluate_dataset.py · sub-skills/training/scripts/
  • lightning_module.py · sub-skills/training/scripts/
  • train_donut.py · sub-skills/training/scripts/

SKILL.md (first lines)

# Donut

Use this repo skill when the task involves NAVER/ClovaAI Donut: OCR-free document understanding with a Swin encoder and BART-like decoder, plus SynthDoG synthetic document generation. This skill is self-contained; do not reopen the original checkout for routine Donut usage.

## Route by task

| User intent | Read or run |
| --- | --- |
| Load a Donut checkpoint, run one-image prediction, compare prompts, or start the Gradio demo | [`sub-skills/inference/SKILL.md`](sub-skills/inference/SKILL.md) |
| Fine-tune, validate dataset JSONL, evaluate a checkpoint, read metrics, or debug configs/checkpoints | [`sub-skills/training/SKILL.md`](sub-skills/training/SKILL.md) |
| Generate synthetic document images, render SynthDoG configs, or adapt resources/fonts/corpora | [`sub-skills/synthdog/SKILL.md`](sub-skills/synthdog/SKILL.md) |
| Inspect public package APIs, signatures, token helpers, and object relationships | [`references/api-reference.md`](references/api-reference.md) |
| Decide which bundled workflow helper or sub-skill owns a request | [`references/workflow-map.md`](references/workflow-map.md) |
| Diagnose install/import, Hugging Face, CUDA, Gradio, dataset, or SynthDoG dependency failures | [`references/troubleshooting.md`](references/troubleshooting.md) |
| Check source baseline, commit, version, and refresh triggers | [`references/repo-provenance.md`](references/repo-provenance.md) |

## Fast start

1. Install a Donut-capable Python environment. For inference only, `pip install donut-python` plus a compatible PyTorch build is the starting point. Training needs CUDA-capable PyTorch. SynthDoG generation also needs `synthtiger` and its image/text dependencies.
2. Smoke-check the environment without the source repo:
   ```bash
   python scripts/runtime_smoke.py
   python scripts/runtime_smoke.py --check all --require-cuda  # when training and SynthDoG are in scope
   ```
3. Route to a sub-skill before giving detailed commands. Each sub-skill owns its own scripts and troubleshooting notes.

## Core facts to preserve

- Package distribution: `donut-python`; import module: `donut`.
- Public exports include `DonutConfig`, `DonutModel`, `DonutDataset`, `JSONParseEvaluator`, `load_json`, and `save_json`.
- `DonutModel.from_pretrained(...)` loads local or Hugging Face checkpoints and requests the Donut repositories' `official` revision.
- `DonutModel.inference(...)` returns a dictionary with a `predictions` list; parsed JSON is the default output.
- Dataset rows use `file_name` plus a JSON-encoded `ground_truth` string containing either `gt_parse` or `gt_parses`.
- Training is GPU-oriented in the original scripts; do not promise CPU training equivalence.
- No model weights, datasets, or large SynthDoG assets are bundled in this skill. Users must provide checkpoints, images, datasets, and SynthDoG resources.

## Bundled helpers

- [`scripts/runtime_smoke.py`](scripts/runtime_smoke.py): cross-workflow environment/API smoke check.
- [`sub-skills/inference/scripts/run_inference.py`](sub-skills/inference/scripts/run_inference.py): single-image inference and prompt comparison.
- [`sub-skills/inference/scripts/launch_demo.py`](sub-skills/inference/scripts/launch_demo.py): Gradio demo launcher.
- [`sub-skills/training/scripts/train_donut.py`](sub-skills/training/scripts/train_donut.py): bundled CUDA-oriented trainer entry point.
- [`sub-skills/training/scripts/check_training_config.py`](sub-skills/training/scripts/check_training_config.py): config and metadata validator.
- [`sub-skills/training/scripts/evaluate_dataset.py`](sub-skills/training/scripts/evaluate_dataset.py): local/HF dataset evaluation helper and validation-only mode.
- [`sub-skills/training/references/configs/`](sub-skills/training/references/configs/): bundled CORD, DocVQA, RVL-CDIP, and TrainTicket config examples.
- [`sub-skills/synthdog/scripts/render_config.py`](sub-skills/synthdog/scripts/render_config.py): render bundled SynthDoG placeholder configs for external resources.
- [`sub-skills/synthdog/scripts/template.py`](sub-skills/synthdog/scripts/template.py): bundled SynthDoG template used by `synthtiger`.

## Boundaries

Use Donut for OCR-free document-image understanding, classification, extraction, visual QA, and synthetic-document pretraining data generation. Do not use this skill for generic PDF parsing, OCR engines, RAG chunking, non-document computer vision models, or unrelated Transformers training unless Donut APIs, checkpoints, config files, errors, or data formats are central to the request.

From VectorSpaceLab/AREX-Skill, MIT.

More from VectorSpaceLab/AREX-Skill

Signals

Stars
341
Forks
26
Open issues
0
Last commit (this folder)
Aug 26, 2026 (2 months ago)
Last release
Sep 2, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from VectorSpaceLab

Cite this page

donut skill. Agent Mart. https://agentmart.dev/skills/vectorspacelab-donut (last updated 2026-10-24)