Hire an AI clothing marketplace photo editor

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A cleaned, even-lit flat-lay photo
• A photo shaped as if worn, with the inside of the collar visible
• Fabric close-ups that keep the real weave and stitching
• A fault report on the set, with lines to add to a retry
• A pass or reject check per marketplace rule, with a fix

How it does the job well

• Start from the user's task and the content, then the layout. Decoration comes last.
• Design every state: empty, loading, error, long text and mobile.
• Keep one visual system. Reuse spacing, type and colour tokens instead of one-off values.
• Check text contrast (at least 4.5:1 for body text) and keyboard focus as you go.

Mistakes it avoids

• Designing only the happy path at desktop width.
• Copying a reference's logo, text or claims instead of learning from its structure.
• Handing over a picture without the sizes, tokens and behaviour a developer needs.

What a good result looks like

An example, not your task: You sell clothing and only have flat-lay photos.

• Cleaned flat lay. A retouched photo with even light and a clean background, plus a note on what changed.
• Worn-shape photo. Your garment shaped as if worn, with a note on what was kept unchanged.
• Fabric close-ups. Close-ups of the weave and stitching, each with the area it shows.
• Fault report. A risk rating per picture, eight named fault checks, a use, retry or retouch call, and fix lines.
• Marketplace check. A pass or reject for each marketplace rule, with a fix for every reject.
• HAND-BACK SUMMARY. Each step's file, any step not run and why, and every MISSING line in one list.

How the work is checked

• Each step passed its own checks, or the run stopped at the step that failed and says why.
• The steps ran in order, and each one used the result of the one before.
• Nothing was invented to fill a gap. Every gap is a MISSING: line.
• Nothing was sent, published or paid for without a clear yes.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Clean the flat lay. Presses out random wrinkles, squares up shoulders and sleeves, evens the light and cleans the background.
• Pause: pick the best photo. You choose the retouched photo that looks most like the real garment and save it.
• Shape it as if worn. Fills the shoulders and chest and curves the sleeves, without changing colour or pattern. It shows the inside of the collar only if your flat lay shows it.
• Pause: save the worn-shape photo. You choose the photo you like best. The next step zooms into it.
• Zoom into the fabric. Frames a close-up of the weave and stitching, adding no detail the garment lacks.
• Pause: gather your set. You put the retouched photo, the worn-shape photo and the close-ups in one folder.
• Inspect for faults. Rates the risk of each picture, runs eight checks and says use, retry or retouch.
• Pause: fix what was flagged. You redo or retouch flagged pictures using the fix lines, then save the final set.
• Check marketplace rules. Checks background, size and framing for the marketplace and marks each rule pass or reject with a fix.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id turn-a-clothing-flat-lay-into-marketplace-ready-photos only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/turn-a-clothing-flat-lay-into-marketplace-ready-photos/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• A flat-lay phone photo of your garment
• The marketplace you list on
• An image model that can draw and edit pictures, such as ChatGPT or Gemini

Tools and access

• ChatGPT image feature: It edits a photo you attach, so it can keep your real garment while it adds shape.
• Gemini image feature: It also edits an attached photo and gives you a second option to compare.
• dLazy command line: It runs the original skill's own path and can preview the cost first. It needs an account and credits.

Functions and deliverables

• Clothing marketplace photo editor: Share a real garment photo and target marketplace. Get photo edits, close-up checks and a review of the final images against relevant rules.
  • A cleaned, even-lit flat-lay photo
  • A photo shaped as if worn, with the inside of the collar visible
  • Fabric close-ups that keep the real weave and stitching
  • A fault report on the set, with lines to add to a retry
  • A pass or reject check per marketplace rule, with a fix
• Product photo retoucher: Bring product photos. Use dLazy’s paid image model to request lighting, wrinkle, layout or background edits.
  • An editing prompt for your image model
  • A line that keeps colour, pattern and structure true
  • A line for a clean white background with a soft shadow
  • Extra lines to fix common problems in the result
  • A checklist to compare the new image with the original
  Source: dLazy.
• Ghost mannequin photo maker: Give it a garment flat lay, garment type and color. Get a prompt for an image tool that aims to keep garment details true.
  • A photo check that says pass, or what to fix first
  • One image prompt that keeps colour, knit, print and label true
  • Size and quality settings for the image maker
  • Fix lines to add if the first try goes wrong
  • A checklist for judging the finished picture
  Source: dLazy.
• Fabric close-up maker: Give it a clothing photo. Use a paid image model to request close-ups of fabric, stitching and weave.
  • Macro close-up image of the chosen area
  • Prompt used, for repeat runs
  • Several area options per garment
  Source: dLazy.
• AI product photo inspector: Share an AI-made product photo. Get eight visual checks, a risk rating and specific prompt fixes, with human review before release.
  • A low, medium or high risk rating
  • A pass or fail result for all eight checks
  • A run, redo or retouch call
  • One to three prompt lines to fix the faults
  Source: dLazy.
• Marketplace photo rule checker: Share product photos and the marketplace. Check image size, colour and layout, then fix supported issues in separate copies.
  • A pass or reject for each photo
  • A plain list of problems
  • Fixed copies where possible
  • A batch summary
  Source: dLazy.
• Brand profile builder: Share your website. Get a reusable profile covering your products, customers, voice and approved claims, with sources.
  • A folder of eight plain files, from a start file for your AI to a claims list and a change log
  • A tag on every fact, and every claim marked approved, pending or banned
  • A two-sentence diagnosis of your biggest gap, and a list of what's missing and who can answer it
  • A short slice for your next step, if you name one

Tasks you can use it for

• You sell clothing and only have flat-lay photos.
• You want fabric close-ups without a macro lens.
• Your last listing photos were rejected or looked off.

Limits to check before starting

• Your garment has a complex print or logo. Image models often warp these, so use a real photo shoot or a photographer instead.
• You have no flat-lay photo and only an idea of the garment. Take a plain phone photo first, because this workflow never invents the garment.
• You only need a single photo cleaned up. Use the Product photo retoucher on its own.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the handoff pack. It holds HANDOFF.md and the 5 skill files in the order they run: Product photo retoucher, Ghost mannequin photo maker, Fabric close-up maker, AI product photo inspector and Marketplace photo rule checker.
• Give the folder to Claude (in Claude Code, or as files in a Claude Project) and say "Follow HANDOFF.md".
• Attach your flat-lay photo. Say which marketplace you list on.
• At each pause, save the picture you like and attach it for the next step.
• Claude writes the instructions. Your image model draws the pictures.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/turn-a-clothing-flat-lay-into-marketplace-ready-photos for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.