analyzing-products
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…
Skill by SupercmoHQ
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/supercmohq-analyzing-products
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/analyzing-products
What's inside
- Skill 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
- 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. · version: 0.1.0 · category: creative
- Instructions
- 48 lines in SKILL.md · view on GitHub
Files (4)
- SKILL.md
- eval_cases.json · evals/
- photo-analysis.md · references/
- url-extract.md · references/
SKILL.md (first lines)
# 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. ## Reference - `references/url-extract.md` — the URL pipeline: `url_extraction` → download the images → filter them with `image_analysis` → write the description. - `references/photo-analysis.md` — the photo pipeline: category, how it's used, moving/opening parts, and the visual details to preserve.
From SupercmoHQ/superCMO-skills, Apache-2.0.
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Compare and alternatives
Featured in
Signals
- Stars
- 206
- Forks
- 23
- Open issues
- 9
- Contributors
- 4
- Last commit (this folder)
- Aug 28, 2026 (2 months ago)
- Last release
- Oct 21, 2026 (6 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 0.1.0
- License
- Apache-2.0
- Repository
- github.com/SupercmoHQ/superCMO-skills
- Website
- getsupercmo.ai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
analyzing-products skill. Agent Mart. https://agentmart.dev/skills/supercmohq-analyzing-products (last updated 2026-10-24)