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Product Tryon Visualization

Checked
tara-shopos·vlatest·data

This skill generates visual representations of product try-ons based on user input, improving understanding and purchase confidence.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add tara-shopos/shopos-prototype --skill product-tryon-visualization -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

tara-shopos

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned tara-shopos/shopos-prototype at 73d9891945.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
73d9891945
Manifest
apps/api/src/skills/product-tryon-visualization/SKILL.md
Scanned
1 file, 506 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill allows users to visualize how products, specifically clothing items, would look when worn. It generates images of a product overlaid onto an image or description of a person. This provides a virtual try-on experience and can aid in purchase decisions.

When to use it

  • A customer wants to see what a specific shirt looks like on someone before buying online.
  • An e-commerce site needs to offer a more interactive product visualization feature.
  • A stylist is creating outfit recommendations and wants to show clients how different pieces will fit together.
  • A user wants to virtually "try on" multiple clothing items quickly without physically changing clothes.

Key capabilities

  • Product overlay onto person images
  • Clothing item visualization
  • Virtual try-on experience

Example prompts

  • “Show me what this blue sweater looks like on a woman with long hair.”
  • “Can you put this red dress on the model in this image?”
  • "Visualize this pair of jeans on someone who is 5'8"."

Tips & gotchas

The quality of the visualization depends heavily on the clarity and resolution of the input images. Providing a clear product image and a representative person image will yield better results.