adapting-formats
Resizes an existing image ad into the other shapes it has to run in — reads the ad first, then regenerates it at each ratio with…
Normalizes a product into generation-ready facts — from an e-commerce URL (a clean description plus curated product images) or from a photo alone (category, how it's used, its moving/opening parts, and key visual details). Use when a product URL or photo needs turning into
Claude Code
$ npx -y skills add SupercmoHQ/superCMO-skills --skill analyzing-products --agent claude-code
Codex
$ npx skills add SupercmoHQ/superCMO-skills --skill analyzing-products -a codex -y
Cursor
$ npx skills add SupercmoHQ/superCMO-skills --skill analyzing-products -a cursor -y
Gemini CLI
$ npx skills add SupercmoHQ/superCMO-skills --skill analyzing-products -a gemini-cli -y
GitHub Copilot
$ npx skills add SupercmoHQ/superCMO-skills --skill analyzing-products -a github-copilot -y
OpenCode
$ npx skills add SupercmoHQ/superCMO-skills --skill analyzing-products -a opencode -y
Windsurf
$ npx skills add SupercmoHQ/superCMO-skills --skill analyzing-products -a windsurf -y
name: analyzing-products description: Normalizes a product into generation-ready facts — from an e-commerce URL (a clean description plus curated product images) or from a photo alone (category, how it's used, its moving/opening parts, and key visual details). Use when a product URL or photo needs turning into inputs for image/video generation, or when another skill needs product facts before generating. license: Apache-2.0 metadata: version: "0.1.0" category: creative summary: "Analyzes an e-commerce link or photo to map out your product's exact physical mechanics and extract clean reference images, building a highly accurate creative brief without any manual research or extraction."
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.
| 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.
Return the result to whoever called you, ready to drop into generation:
Don't rank the product's market position — the calling skill decides that from packaging cues. Your job is the objective facts.
instead and switch to photo mode.
cleanest, or hand off the description alone and say plainly that no clean image survived.
determine and ask for a clearer shot rather than guessing the mechanic.
with `image_analysis` → write the description.
and the visual details to preserve.
Open-source skills that empower any AI agent (Claude, Cursor, Hermes, etc.) to generate end-to-end marketing campaigns - UGC videos, ad videos, product photography and more.
Resizes an existing image ad into the other shapes it has to run in — reads the ad first, then regenerates it at each ratio with…
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