Name: Mistral PDF OCR
Author: Tristan Manchester

According to AlphaMoat, as of 2026-09, Mistral PDF OCR has 1,333 downloads and 0 stars — ranked #4,875 of 5,078 AI skills overall, and #405 of 435 in Office.

Installs

159

Downloads

1.3K

Stars

Overall Rank

#4,875

Top 97% overall · Top 94% in Office

Platform	Installs	Downloads	Stars	Comments
ClawHub ↗	33	1.2K	-	-
SkillHub ↗	159	1.3K	-	—

Platforms count differently and update at different times.

What this skill does

It supports scanned-PDF OCR, Markdown conversion and structured extraction. Documents go to Mistral and outputs need a controlled directory; malformed responses may escape the target path, and mistralai should be pinned. Requires Python, MISTRAL_API_KEY, and a vetted pinned SDK. Fix and verify output filename containment before writing artifacts; choosing a directory alone does not resolve it. Treat uncertain structured fields as null and verify extraction quality.

Profile published 2026-10-27

What you can do with it

OCR scanned PDFs
Convert content to Markdown or structured data

When to use it

When transmission is approved and output paths checked

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Review transmission approval and the output-containment fix first; after both pass, extract the table into the approved directory.

Monthly Trend

Downloads1.3K

Stars-

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.

Month	Downloads	MoM	Stars
2026-09	1.3K	—	-
2026-08	1.1K	+9.4%	-
2026-07	1.0K	+17.7%	-
2026-06	877	+9.5%	-
2026-05	801	—	-
2026-03	615	—	-

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.

3.1 KB · ClawHub v1.0.0

---
name: extracting-mistral-ocr
description: >-
  Extracts text, tables, and images from PDFs (including scanned PDFs) using the Mistral OCR API.
  Use when user asks to OCR a PDF/image, extract text from a PDF, parse a scanned document,
  convert a PDF to Markdown, or extract structured fields from a document.
compatibility: >-
  Requires network access and a MISTRAL_API_KEY environment variable. Expects Python 3.9+ and the mistralai package.
allowed-tools: "Read,Write,Bash(python:*)"
metadata:

Show all (80 lines)

  author: generated-by-chatgpt
  version: 0.1.0
  api: mistral
  default-model: mistral-ocr-latest
---

# Mistral OCR PDF extraction

## Quick start (default)

Run the bundled script to OCR a local PDF and write Markdown + JSON outputs:

```bash
python {baseDir}/scripts/mistral_ocr_extract.py --input path/to/file.pdf --out out/ocr
```

Output directory layout:

- `combined.md` (all pages concatenated)
- `pages/page-000.md` (per-page markdown)
- `raw_response.json` (full OCR response)
- `images/` (decoded embedded images, if requested)
- `tables/` (separate tables, if requested)

## Workflow

1. **Pick input mode**
   - **Local PDF** (most common): upload via Files API, then OCR via `file_id`.
   - **Public URL**: OCR directly via `document_url`.

2. **Choose output fidelity** (defaults are safe for RAG)
   - Keep `table_format=inline` unless the user explicitly wants tables split out.
   - Set `--include-image-base64` when the user needs figures/diagrams extracted.
   - Use `--extract-header/--extract-footer` if header/footer noise hurts downstream search.

3. **Run OCR**
   - Use `scripts/mistral_ocr_extract.py` to produce a deterministic on-disk artefact set.

4. **(Optional) Structured extraction from the whole document**
   - If the user wants fields (invoice totals, contract parties, etc.), provide an annotation prompt.
   - The OCR API can return a document-level `document_annotation` in addition to page markdown.

   Example:

   ```bash
   python {baseDir}/scripts/mistral_ocr_extract.py \
     --input invoice.pdf \
     --out out/invoice \
     --annotation-prompt "Extract supplier_name, invoice_number, invoice_date (ISO-8601), currency, total_amount. Return JSON." \
     --annotation-format json_object
   ```

## Decision rules

- **If the PDF is local and not publicly accessible**, upload it (the script does this automatically).
- **If the PDF URL is private or requires authentication**, do not pass it as `document_url`; upload instead.
- **If output quality is critical**, prefer `table_format=html` for downstream parsing over brittle regex.

## Common failure modes

- **Missing `MISTRAL_API_KEY`**: set it in the environment before running.
- **URL OCR fails**: the URL likely is not publicly accessible; upload the file.
- **Large files**: upload supports large files, but very large PDFs may need page selection (`--pages`) or batch processing.

## References

- API + parameters: `references/mistral_ocr_api.md`
- Output mapping rules (placeholders to extracted images/tables): `references/output_mapping.md`
- Example annotation prompts for common document types: `references/annotation_prompts.md`