git:20260720.a66efb1 to git:20260726.e702451

7 added, 13 removed. Audit A to A.

---

name: product-image-processor

description: Download, resize, and remove backgrounds from product images at scale. Use when the user asks to "process product images", batch-download images from the schedule, strip backgrounds, or standardize product photos.

allowed-tools:

- Read

- Write

- Bash

- Glob

- Grep

- WebFetch

- AskUserQuestion

- - mcp__google-sheets__get_sheet_data

- - mcp__google-sheets__list_sheets

---

- # /product-image-processor — Product Image Processor

+ # /as:product-image-processor — Product Image Processor

- Download product images from a Google Sheet, normalize sizing, and remove backgrounds. Saves output at each processing stage.

+ Read product image records from the nearest project's `product-library.csv`, download them, normalize sizing, and remove backgrounds. Saves output at each processing stage without mutating the library.

- Works with the **master Google Sheet** — the 33-column schema defined in `../../schema/product-schema.md`. Image URLs are in column AC, product names in column E. Read `../../schema/sheet-conventions.md` for CRUD patterns with MCP tools.

+ Read `../../schema/product-schema.md` and `../../schema/csv-conventions.md`. Resolve the nearest ancestor containing `PROJECT.md`, strictly validate its `product-library.csv`, and address fields by the exact names `Image URL` and `Product Name`, never by position or letters.

## Step 1: Get Input

- If no arguments provided, ask the user:

- 1. **Spreadsheet ID** — the Google Sheets ID (from the URL: `docs.google.com/spreadsheets/d/{ID}/...`). 2. **Image URL column** — which column contains image URLs (default: `AC` in the master schema, or the user can specify)

- 3. **Name column** (optional) — which column has product names for file naming (default: `E` in the master schema). If not provided, derive names from the image URL/filename.

- 4. **Output location** — where to save the images. Suggest `./product-images-YYYY-MM-DD/` as default but let the user pick any path.

- 5. **Header row** — whether row 1 is a header (default: yes, row 2 in master schema)

+ If no arguments are provided, use the nearest project's `product-library.csv` and ask only for the output location when it cannot be inferred. Suggest `./product-images-YYYY-MM-DD/`.

- ## Step 2: Read URLs from Google Sheet

+ ## Step 2: Read URLs from CSV

- Use `mcp__google-sheets__list_sheets` to inspect the sheet, then `mcp__google-sheets__get_sheet_data` to read the image URL column and optional name column.

+ Run `python3 "${CLAUDE_PLUGIN_ROOT}/skills/master-schedule/scripts/csv-library.py" validate product --project <project-root>` before reading. Parse the entire UTF-8 CSV strictly and select the named `Image URL` and `Product Name` fields.

Build a list of `{ index, url, name }` entries. Skip empty rows.

## Step 3: Create Output Folders

Create the output directory at the user's chosen path with 3 subfolders:

```

<output-path>/

├── originals/ # Raw downloads

├── resized/ # Normalized sizing

└── nobg/ # Background removed

```

If the folder already exists, append a suffix: `-2`, `-3`, etc.

## Step 4: Download Images

Download each image using `curl` in Bash:

```bash

curl -L -o "<output-path>" "<url>"

```

**IMPORTANT:** Use `curl`, NOT WebFetch. WebFetch processes content through an AI model which corrupts binary image data.

Name files as: `001-product-name.png`, `002-product-name.png`, etc.

- Slugify the product name: lowercase, replace spaces/special chars with hyphens, strip consecutive hyphens

- If no name column, extract a name from the URL filename (strip extension and query params)

- If the URL gives no usable name, use `001-image.png`, `002-image.png`, etc.

If the downloaded file is not a PNG (check extension or content type), convert it to PNG during the resize step.

## Step 5: Resize Images

Run a Python script to resize all images in `originals/` → `resized/`:

```python

from PIL import Image

import os, sys

input_dir = sys.argv[1] # originals/

output_dir = sys.argv[2] # resized/

max_edge = int(sys.argv[3]) if len(sys.argv) > 3 else 2000

for fname in sorted(os.listdir(input_dir)):

if not fname.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.gif', '.bmp', '.tiff')):

continue

try:

img = Image.open(os.path.join(input_dir, fname))

img = img.convert("RGBA")

w, h = img.size

longest = max(w, h)

if longest > max_edge:

scale = max_edge / longest

new_w, new_h = int(w * scale), int(h * scale)

img = img.resize((new_w, new_h), Image.LANCZOS)

out_name = os.path.splitext(fname)[0] + ".png"

img.save(os.path.join(output_dir, out_name), "PNG")

print(f"OK: {fname} → {out_name} ({img.size[0]}x{img.size[1]})")

except Exception as e:

print(f"FAIL: {fname} — {e}")

```

Rules:

- Max **2000px** on the longest edge (configurable if user requests)

- Preserve aspect ratio

- Do NOT upscale — if already smaller than max, keep original dimensions

- Convert everything to PNG (RGBA mode for transparency support)

## Step 6: Remove Backgrounds

Check if `rembg` is installed. If not, install it:

```bash

pip3 install rembg onnxruntime

```

Then run background removal on all resized images → `nobg/`:

```python

from rembg import remove

from PIL import Image

import os, sys, io

input_dir = sys.argv[1] # resized/

output_dir = sys.argv[2] # nobg/

for fname in sorted(os.listdir(input_dir)):

if not fname.lower().endswith('.png'):

continue

try:

input_path = os.path.join(input_dir, fname)

with open(input_path, 'rb') as f:

input_data = f.read()

output_data = remove(input_data)

img = Image.open(io.BytesIO(output_data))

img.save(os.path.join(output_dir, fname), "PNG")

print(f"OK: {fname}")

except Exception as e:

print(f"FAIL: {fname} — {e}")

```

**Note:** The first run of rembg downloads the u2net model (~170MB). Warn the user this may take a minute.

## Step 7: Report Results

After processing, print a summary:

```

## Product Image Processing Complete

📁 Output: ./product-images-YYYY-MM-DD/

| Stage | Success | Failed |

|-------------|---------|--------|

| Downloaded | 12 | 1 |

| Resized | 12 | 0 |

| BG Removed | 12 | 0 |

### Failures

- 003-chair-arm.png: Download failed (404 Not Found)

```

Include the full path to the output folder so the user can open it.

## Error Handling

- **Download failures:** Log and continue. Don't block the pipeline for one bad URL.

- **Resize failures:** Log and continue. Skip that image in the bg-removal step.

- **rembg failures:** Log and continue. Some images (vectors, icons) may not process well.

- - **Sheet read errors:** Stop and report. Ask the user to verify the spreadsheet ID and column.

+ - **CSV validation errors:** Stop and report. Leave the source byte-for-byte unchanged.

## Notes

- Process images sequentially (not parallel) to avoid overwhelming the network or CPU

- For large batches (50+ images), print progress every 10 images

- The rembg model download only happens once — subsequent runs reuse the cached model