Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add SupercmoHQ/superCMO-skills --skill analyzing-productsgit clone --depth 1 https://github.com/SupercmoHQ/superCMO-skillsWrote 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/supercmohq/supercmo-skills/analyzing-products)<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/analyzing-products"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/analyzing-products/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/supercmohq/supercmo-skills/analyzing-products"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/analyzing-products.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 | $0.00077 | $0.00707 |
| Opus 5 | $0.00039 | $0.00353 |
| Sonnet 5 | $0.00015 | $0.00141 |
| Haiku 4.5 | $0.00008 | $0.00071 |
Grade A, and why
analyzing-products scanned grade A with 0 findings 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 10d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Analysis
Turn a product — an e-commerce URL or a bare photo — into clean, reusable facts for downstream image/video generation: a tight description, curated product images, and how the product is physically used. It's a building block: other skills call it at the input stage, before any generation.
Workflow
Step 1: Pick the mode and run it
| Input | Reference | What you produce |
|---|---|---|
| An e-commerce URL (Amazon, Shopify, AliExpress, any product page) | references/url-extract.md |
A two-paragraph description + up to 5 downloaded, filtered product images |
| A product photo only (no URL, no description) | references/photo-analysis.md |
Category + how it's used + moving/opening parts + key visual details |
Read only the matching reference and follow it end to end. If both a URL and a photo are given, run the URL mode (richer) and keep the photo as one more reference image; if neither is given, there's nothing to analyze — ask for one. Don't pause for confirmation — a URL (or photo) plus generation intent means extract and proceed.
Step 2: Hand off
Return the result to whoever called you, ready to drop into generation:
- URL mode → the description and the kept image files (local paths, usable as reference images).
- Photo mode → the category, how it's used, any moving or opening parts, and the key visual details.
Don't rank the product's market position — the calling skill decides that from packaging cues. Your job is the objective facts.
Edge cases
- The URL can't be extracted (no result, or the extractor isn't set up) → ask for a product photo instead and switch to photo mode.
- Every image fails the filter (faces, wrong variant, not a product shot) → keep the single cleanest, or hand off the description alone and say plainly that no clean image survived.
- A supplied photo is too unclear to read (blurry, cropped, ambiguous) → say what you can't determine and ask for a clearer shot rather than guessing the mechanic.
- Neither a URL nor a photo → ask for one; there is nothing to analyze.
What ships with it
3 files 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.
- 10d ago First seen · 57 lines · 77 tokens per session scan A 72007eee87d8
analyzing-products is a skill published in the GitHub repository SupercmoHQ/superCMO-skills (37 stars, last pushed 13d ago), licensed Apache-2.0. It adds 77 tokens to every session and 707 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
beat-sync-reel
Generates Instagram Reels where product image cuts are synced to audio beats. Accepts audio as a local file, URL, or search query. Uses librosa for beat detection, FFmpeg Ken Burns for scene animation, and Pillow for text overlays. No AI video generation — fully free, fast, and scalable.
product-reel-generator
Generates Instagram-ready product reels from any e-commerce product page URL. Scrapes product images, classifies by type, generates AI-animated clips via Higgsfield API, creates text overlays with style presets, and composes a 15-20 second reel with music. Supports model-based and product-only reels.
product-photoshoot
Create faithful studio, lifestyle, and on-model product photography through the GooseWorks Product Photos workflow, then approve selected results for reuse in future creative work.
creative-scene
Generate realistic product-in-use lifestyle scenes. Places the product in believable contexts of use (who, where, when, doing what) with natural lighting and real texture, for lifestyle/editorial/ecommerce. Use when the user says "lifestyle shot", "product in use", "scene", "show it being used", "in-context…
creative-shot
Generate multiple angles and shot types of a single product for ecommerce-ready image sets. Produces a coherent set (hero 3/4, front, side, back, detail macro, scale, packaging, in-hand) keeping product identity consistent across shots. Use when the user says "different angles", "product shots", "ecommerce image set"…
extract-product-data-and-generate-listing-image
Extract product details from a supplier data sheet, then generate a branded e-commerce listing image with the product name, price, and key specs.