Hire an AI product photo batch reviewer

Everything it can hand back

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

• One matching photo per product
• A contact sheet to pick from
• A fault report with a use, retry or retouch call per photo
• A pass or reject check per marketplace rule, with a fix

How it does the job well

• Start from the user problem and the evidence for it, then the solution.
• Write decisions down with the reason and the options you rejected.
• Keep scope small enough to ship and learn. Name what is out of scope.
• Define how success will be measured before building.

Mistakes it avoids

• Treating opinions or one loud request as proven demand.
• Specs that skip edge cases, error states or dependencies.
• Roadmaps with dates but no owners or confidence levels.

What a good result looks like

An example, not your task: You have a long product list and no photos for most of it.

• step-01-product-photo-batch-maker.md. One matching photo per row of your sheet, a contact sheet to pick from, and the cost used against your limit.
• step-02-ai-product-photo-inspector.md. A risk rating for each photo, eight checks, and a call of use, retry or retouch with fix lines.
• step-03-marketplace-photo-rule-checker.md. A pass or reject for each marketplace rule on your final photos, 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

• Every photo shows the right product, in the right colour, and never a shape or colour the real item lacks.
• Backdrop, light and angle match across the set, and the cost stayed under your limit.
• No photo with garbled text or warped parts was kept.
• Check that your marketplace allows AI-drawn product photos and whether it asks for a label. Confirm each photo you list is allowed and, where a label is required, labelled as AI-generated.
• 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

• Make the batch. Reads your sheet, keeps one look word for word the same, changes only the product line for each row, runs a small test first and builds a contact sheet.
• Pause: approve and save. You check the test batch, approve the look and save the finished photos.
• Inspect for faults. Rates the risk of each photo, runs eight checks and says use, retry or retouch.
• Pause: redo flagged photos. You redo the flagged photos with the fix lines, then save the final set.
• Check marketplace rules. Checks background, size and framing for your 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 make-matching-photos-for-a-product-sheet-and-check-them 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/make-matching-photos-for-a-product-sheet-and-check-them/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 sheet of products with names, descriptions and a reference photo for each
• The look you want in one sentence
• A spending limit for the batch
• An image model that can draw pictures, such as ChatGPT or Gemini

Functions and deliverables

• Product photo batch reviewer: Share product rows, real photos and target marketplace. Get a pilot, generated batches where tools allow, defect checks and a listing-rule review.
  • One matching photo per product
  • A contact sheet to pick from
  • A fault report with a use, retry or retouch call per photo
  • A pass or reject check per marketplace rule, with a fix
• Product catalogue photo maker: Give it a spreadsheet of products. Get one matching photo per product, with a cost limit. Your image model draws them.
  • A batch script with fixed style and variable parts
  • One image per product
  • Retry and resume handling
  • A price check before you run
  • A report.csv marking each product ok or fail
  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.
• Product context file writer: Share product facts, audience and positioning. Get a reviewed context file that other marketing skills can reference.
  • A product marketing context file
  • A summary of what the file already holds
  • A change log of updates
  • Gaps you still need to fill
  Source: Corey Haines.

Tasks you can use it for

• You have a long product list and no photos for most of it.
• You want one consistent look across the whole shop.
• You list on a marketplace that rejects photos for small faults.

Limits to check before starting

• You only have a few products: take real photos with a phone or hire a photographer, since real photos build more trust.
• You already have photos and only need to know if a shop will accept them: use the Marketplace photo rule checker on its own.
• You want to judge photos that you made in another tool: use the AI product photo inspector 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's a zip with HANDOFF.md and every skill file this workflow uses.
• Give the folder to Claude (in Claude Code, or as files in a Claude Project) and say "Follow HANDOFF.md".
• Attach your product sheet and a reference photo for each product. Set your cost limit.
• Approve the test batch before the full run. Ask for changes to the look at this point, not later.
• Redo any flagged photo with the fix lines, then save the final set for the rules check.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/make-matching-photos-for-a-product-sheet-and-check-them 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.