visual-attention-auditlisted
Install: claude install-skill AlexeyGOblov/visual-attention-audit
# Visual attention audit
Three questions, answered with numbers:
1. **How cluttered is this frame, and does its lettering still exist at the size it is shown?**
2. **Does it hold up among its neighbours?**
3. **Where does a model trained on gaze expect the eye to go?** (optional)
The first two run on plain images: no browser, no network, no model weights, just numpy,
scipy and Pillow. The third is installed separately.
## First: verify, then say what the frame is
```bash
python scripts/clutter.py --selftest
```
Four checks must pass. If they do not, nothing below means anything. Details and the
expected figures: [reference/validation.md](reference/validation.md).
Then every command needs `--kind`. **It is never guessed.** Ask whoever brought the image;
if they are unsure, look at it, say what you think it is, and use what they confirm.
| `--kind` | What it is | Measured at | Colour mass |
|---|---|---|---|
| `marketplace_card` | a product card, 3:4, artwork over a photo | 190, 240, 390 px | rank against neighbours |
| `results_grid` | a screenshot of results, a shelf, a competitor set | as captured, then cut | rank of the tiles |
| `ui_screen` | a working screen of an app or a site | native, plus 390 px | **high is the fault** |
| `landing_page` | a full-page capture, much taller than wide | 390, 1280 px | not a ranking |
| `banner_ad` | an advertisement, a poster, a creative | 300, 728, 1080 px | rank in the batch |
| `packaging` | the packaging of a product | native