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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

IndexedApache-2.0★ 206
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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

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.

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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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    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
    First seen
    Oct 24, 2026
    Last checked
    Oct 24, 2026

    More from SupercmoHQ

    Cite this page

    analyzing-products skill. Agent Mart. https://agentmart.dev/skills/supercmohq-analyzing-products (last updated 2026-10-24)