Generate and revise a fact-checked, channel-ready image suite for one SKU while preserving product identity, claims, and visual consistency.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install product-image-suite-generation-editing
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install product-image-suite-generation-editing using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Product Image Suite Generation & Editing?
Product Image Suite Generation & Editing is an Openclaw Skills workflow for turning one SKU into a coordinated set of ecommerce visuals instead of disconnected single images. It supports white-background hero images, scene images, feature and detail graphics, material close-ups, size-scale visuals, lifestyle scenes, packaging images, comparisons, and localized assets.
The skill uses the AI Hive Nano Banana Pro model through a fixed API workflow. Each render reuses a suite ID, SKU facts, identity locks, approved reference images, channel requirements, editable elements, exclusions, and claim records. This creates auditable product image sets for Amazon, Tmall, JD, Douyin, Xiaohongshu, Shopify, and other listing or product-detail channels without connecting to ecommerce platform accounts.
Product Image Suite Generation & Editing Use Cases
- Create Amazon white-background listing hero image candidates with accurate components, packaging, and product colors.
- Produce Tmall or JD feature, detail, and material images tied to approved selling points and engineering records.
- Build Douyin or TikTok Shop vertical lifestyle visuals with reserved overlay zones for later copy and platform elements.
- Design Xiaohongshu localized seeding covers for specific markets, languages, and channel aesthetics.
- Generate Shopify comparison images that show verified states of the same SKU without unsupported competitor claims.
- Prepare consistent PDP, A+ content, marketplace listing images, product cards, and social commerce assets.
- Revise a suite when factual errors, visual drift, information overload, unsupported claims, or channel-fit problems are discovered.
- Scale image production across color variants while requiring new suite IDs for structural product variants.
How Product Image Suite Generation & Editing Works
- Define an image responsibility matrix with sequence number, slot, unique objective, permitted claims, composition, and target channel.
- Assign each source image a precise source role, such as front-view product facts, side details, packaging contents, material texture, or an approved style anchor.
- Create or reuse a suite ID and SKU record containing the verified product facts, component counts, colors, labels, packaging, and structural constraints.
- Select the platform and slot, then provide the frame job, identity lock, editable elements, layout, set design, illumination, exclusions, and any required parameters such as aspect ratio.
- For factual slots such as feature, detail, material, size-scale, or comparison, attach the required approved claim, claim record, measurement record, or comparison rule.
- Run the product suite script with the render command. The script uploads only the explicitly authorized references to the fixed AI Hive API and submits a Nano Banana Pro generation task.
- Poll the task status and download the generated result, retaining the suite ID, sequence, slot, and version in the filename.
- Compare every output against the source facts and continuity ledger, checking quantity, silhouette, interfaces, accessories, color, material, labels, logo, packaging, and claims.
- Move the asset through draft, factual-pass, design-pass, channel-pass, and approved states before publication.
- If revisions are needed, classify the issue and change one variable at a time; update the ledger and rejection notes instead of regenerating the entire suite without traceability.
Product Image Suite Generation & Editing Setup
- Set the skill path and prepare authorized product reference images.
- Install the required Python dependency:
pip3 install requests
- Authenticate against AI Hive with an API key:
python3 "$SKILL_PATH/scripts/product_suite.py" auth --api-key sk-api-your-ai-hive-key
- Run a non-generating brief check when validating prompts, required fields, or layout logic. Replace
renderwithbrief; this does not upload references or create a generation task. - Run a render by supplying
--platform,--suite-id,--sku,--slot,--sequence, one or more--source-imagevalues, matching--source-rolevalues, and the product, layout, lighting, lock, and exclusion fields. Example:
python3 "$SKILL_PATH/scripts/product_suite.py" render \\
--platform amazon --suite-id TOOL-D12-US --sku TOOL-D12-ORANGE \\
--slot hero-white --sequence 1 \\
--source-image ./driver-kit-open.png ./driver-body-side.png \\
--source-role "verified package contents" "verified side controls and interfaces" \\
--sku-record "verified SKU facts and component count" \\
--frame-job "create the first factual listing baseline" \\
--identity-lock "outline, color, buttons, interfaces, accessories, and packaging" \\
--editable-elements "clean dust, organize shadows, and adjust placement" \\
--layout "all real components visible with consistent margins" \\
--set-design "seamless white tabletop" \\
--illumination "large soft overhead light with a subtle contact shadow" \\
--exclusions "no invented accessories, claims, labels, or promotional elements"
- Poll an asynchronous task with its returned task ID:
python3 "$SKILL_PATH/scripts/product_suite.py" status --task-id <taskId>
- Review and approve the downloaded image before adding a version and publication status. The API is fixed to
https://ai-hive.iclip.cn/api, the model is fixed topublic_model_nano_banana_pro, and the workflow does not connect to ecommerce platform accounts.
Product Image Suite Generation & Editing Data Schema & Taxonomy
The skill organizes each image suite around a reusable suite-id and SKU-specific factual record.
| Field or artifact | Purpose |
|---|---|
suite-id |
Groups all coordinated images for one product suite and frozen visual baseline. |
sku |
Identifies the exact product, color, or variant being rendered. |
slot |
Defines the image responsibility, including hero-white, hero-scene, feature, detail, material, size-scale, lifestyle, comparison, packaging, and localized. |
sequence |
Controls the ordered position of the asset in a listing or product-detail set. |
source-image and source-role |
Pair authorized reference files with explicit factual responsibilities. |
sku-record |
Stores verified product facts such as components, quantity, color, structure, packaging, labels, and interfaces. |
identity-lock |
Freezes product attributes that must not drift between images. |
color-lock |
Records primary, secondary, metallic, and non-shiftable product color areas. |
camera-family |
Defines primary, secondary, and permitted close-up viewpoints. |
light-vector |
Captures light direction, shadow softness, and highlight behavior. |
surface-language |
Defines backgrounds, surfaces, layering, and prop density for the suite. |
copy-zone |
Reserves safe areas for approved channel copy, pricing, buttons, or overlays. |
claim-ledger |
Maps each claim to an approver, document version, and valid scope. |
revision-log |
Records rejection reasons and converts recurring issues into must-keep or negative constraints. |
approved-claim and claim-record |
Required for claim-bearing feature, detail, material, or comparison visuals. |
measurement-record |
Required for size-scale images. |
comparison-rule |
Required for comparison images and limits the comparison scope. |
Recommended filenames follow suite-id_sequence_slot_version, such as TOOL-D12-US_03_feature_v2.png. Track the lifecycle with draft, factual-pass, design-pass, channel-pass, and approved. Keep the ledger and source references alongside final PNG files so future Openclaw Skills workflows can distinguish frozen facts from editable design choices.
Product Image Suite Generation & Editing Advanced Features
- Fixed AI Hive API integration with the
public_model_nano_banana_promodel for repeatable generation behavior. briefmode for validating prompts and required inputs without uploading assets or creating tasks.- Asynchronous task submission, status polling, and result downloading through the product suite script.
- Multi-channel support for Amazon, Tmall, JD, Douyin, Xiaohongshu, Shopify, and related ecommerce workflows.
- Reusable continuity ledger covering identity, color, camera, lighting, surface language, copy zones, claims, and revisions.
- Source-role pairing that prevents style references from being mistaken for product facts.
- Claim, measurement, and comparison safeguards for evidence-based commercial visuals.
- Overlay reservations and aspect-ratio parameters for vertical social commerce assets such as 9:16 product cards.
- One-variable revision discipline that isolates factual, continuity, information-density, unsupported-claim, and channel-fit issues.
- Representative-SKU workflow for freezing a validated suite matrix before scaling to additional SKUs.
- Color-variant reuse of structural rules while preserving separate factual references for each color.
- New suite IDs for structural variants to prevent accidental cross-SKU fact inheritance.
- Explicit exclusions that block invented accessories, extra components, unsupported text, certifications, dimensions, prices, performance claims, or competitor comparisons.
- Human approval gates that separate visual quality from factual and publication readiness.
SKILL.md
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