Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AlpacaLabsLLC/skills-for-architectsnpx agentmods add skills/alpacalabsllc/skills-for-architects/product-image-processorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alpacalabsllc/skills-for-architects/product-image-processor)<a href="https://agentmods.dev/skills/alpacalabsllc/skills-for-architects/product-image-processor"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/product-image-processor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alpacalabsllc/skills-for-architects/product-image-processor"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/product-image-processor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.01507 |
| Opus 5 | $0.00023 | $0.00754 |
| Sonnet 5 | $0.00009 | $0.00301 |
| Haiku 4.5 | $0.00005 | $0.00151 |
Grade A, and why
product-image-processor scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Download each image using `curl` in Bash: How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/as:product-image-processor — Product Image Processor
Harness note: use
/as:<skill>on Claude Code and$<skill>on Codex. Resolve<skill-root>as the directory containing this loadedSKILL.mdand<plugin-root>as the plugin root that containsskills/, and use equivalent native tools when host tool names differ.
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.
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 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 CSV
Run python3 "<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:
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.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 168 lines · 45 tokens per session scan A 0afc2600b9be
product-image-processor is a skill published in the GitHub repository AlpacaLabsLLC/skills-for-architects (351 stars, last pushed 8d ago), licensed MIT. It adds 45 tokens to every session and 1,507 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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