Imported from znlgis/my-opencode-deepseek-config (
opencode/skills/vision-prep/SKILL.md). Install upstream withnpx skills add znlgis/my-opencode-deepseek-config --skill vision-prep. Copyright stays with the author.
Vision Preprocessing (large images / PDF pages)
Prepare images and PDF pages so the DeepSeek vision model can actually read them. Two hard constraints drive this:
- DeepSeek rejects PDF input — only JPEG/PNG/GIF/WebP are accepted. A PDF must be rasterized to PNG first.
- DeepSeek downscales each image to ~800x800 pixel budget before tokenizing. Small text in a large image becomes unreadable. Slicing into overlapping tiles gives each tile its own ~800x800 budget.
When to use
- The user attaches a large image / screenshot and small text is unreadable.
- The user asks to read content inside a PDF (text or embedded images).
- The vision agent reports it cannot make out details in an image.
Prerequisites
Python 3 with pymupdf and pillow (already installed). Scripts live in
scripts/ next to this file.
Workflow
1. PDF -> PNG pages
python <skill_dir>/scripts/pdf2png.py <input.pdf> <output_dir> [--pages 1-3,5] [--zoom 2.0]Renders each page (or --pages subset) to page-<n>.png. --zoom controls
resolution (2.0 ≈ 144dpi, good default for text). Rasterizing preserves page
layout, which matters when the goal is "what is in this image within the
page context".
2. Large image / rendered page -> tiles
python <skill_dir>/scripts/tile.py <input.png> <output_dir> [--grid 2x2] [--overlap 0.10]Slices into an NxN grid of overlapping tiles (tile-<row>-<col>.png). Use
when the image's long edge exceeds ~1600px or it contains dense small text.
The 10% overlap prevents text straddling a cut line from being truncated.
3. Read tiles with the vision model
Send each tile to the model separately (e.g. via the read tool / vision
agent), so each tile gets its own ~800x800 budget. Extract content per tile,
then merge the results in the text layer. For very dense content (e.g.
300-DPI legal documents), use a finer grid (3x3) or request tiles one at a
time.
Decision guide
| Input | Action |
|---|---|
| Small image (< ~1600px long edge) | Send directly, no prep |
| Large image / dense small text | tile.py into 2x2 (or finer) grid |
| Grid size (when tiling) | --grid NxN with N = ceil(long_edge / 800), so each tile stays <= ~800px |
| PDF (any) | pdf2png.py to PNG, then tile if page is large |
| Byte/size limit exceeded | Already handled by this repo's attachment.image resize (1600px / 2 MiB); no action needed |
Notes
- This repo caps attachments at 1600x1600 / 2 MiB (
attachment.imageinopencode.jsonc), below opencode's own default — so the upload limit is not the problem; the model's ~800x800 downscale is. Tiling is the fix for readability, not resizing. - Always use absolute paths.
- After tiling, describe each tile's content and combine; do not send the original full-size image expecting the model to read fine print.