# Product Image Processor

> Download, resize, and remove backgrounds from product images at scale

- **Type:** Skill
- **Install:** `agentstack add skill-alpacalabsllc-skills-for-architects-product-image-processor`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [AlpacaLabsLLC](https://agentstack.voostack.com/s/alpacalabsllc)
- **Installs:** 0
- **Category:** [Content & Media](https://agentstack.voostack.com/c/content-and-media)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [AlpacaLabsLLC](https://github.com/AlpacaLabsLLC)
- **Source:** https://github.com/AlpacaLabsLLC/skills-for-architects/tree/main/plugins/06-materials-research/skills/product-image-processor
- **Website:** https://alpa.llc/skills

## Install

```sh
agentstack add skill-alpacalabsllc-skills-for-architects-product-image-processor
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# /product-image-processor — Product Image Processor

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

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.

## 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)

## Step 2: Read URLs from Google Sheet

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.

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:

```
/
├── 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 "" ""
```

**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.

## 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

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [AlpacaLabsLLC](https://github.com/AlpacaLabsLLC)
- **Source:** [AlpacaLabsLLC/skills-for-architects](https://github.com/AlpacaLabsLLC/skills-for-architects)
- **License:** MIT
- **Homepage:** https://alpa.llc/skills

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** yes
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-alpacalabsllc-skills-for-architects-product-image-processor
- Seller: https://agentstack.voostack.com/s/alpacalabsllc
- Browse the marketplace: https://agentstack.voostack.com/browse

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