name: ecommerce-image-workflow en_name: "Ecommerce Image Workflow" description: | Reference-product ecommerce image workflow for generating a compact set of product-faithful main, feature, and lifestyle images from real product reference photos. V1 requires uploaded product imagery and intentionally defers brief-only concept generation and platform-specific batch exports. triggers:
- "ecommerce product images"
- "product image set"
- "product photography workflow"
- "product main image"
- "product feature shot"
- "reference product commerce images"
- "lifestyle product image"
- "amazon product images"
- "shopify product images"
- "taobao product images" od: mode: image surface: image category: image-generation scenario: marketing preview: type: html entry: example.html design_system: requires: false example_prompt: | Use the Ecommerce Image Workflow to turn my uploaded product reference photo into a compact ecommerce image set: one main packshot, one feature highlight image, and one lifestyle scene. Preserve the exact product identity, color, material, logo placement, structure, and proportions.
Ecommerce Image Workflow
Create a compact ecommerce image set from real product reference imagery. This V1 skill is intentionally narrow: it supports reference-product mode only. If the user only describes a product and does not provide a product photo, ask for one and stop. Do not create a brief-only concept product in this version.
Resource map
ecommerce-image-workflow/
|-- SKILL.md
|-- example.html
`-- references/
`-- checklist.md
What this skill produces
By default, generate three ecommerce-ready image assets for one product:
- Main image - clean product-first packshot on white or soft neutral background.
- Feature image - one selling point shown clearly with controlled callout space, without relying on tiny unreadable in-image text.
- Lifestyle image - product shown in a plausible use context while keeping the product faithful to the reference.
Also create:
image-manifest.jsondescribing reference inputs, slots, prompts, outputs, aspect ratios, and fidelity notes.ecommerce-gallery.htmlas a small preview gallery linking the generated files and summarizing the image roles.
Input contract
Required:
- At least one uploaded product reference image in the active project.
Ask only for missing essentials:
- Product name or short label if it is not obvious.
- Main selling point if the feature image cannot be inferred safely.
- Target marketplace or aspect only if the user asks for platform-specific framing.
Do not ask broad discovery questions. Keep the workflow moving.
Workflow
Step 0 - Confirm reference-product mode
Before planning, verify that the current project includes a real product reference image.
If no product image is available, reply:
Please upload at least one product reference image first. This V1 workflow preserves a real product from reference photos; brief-only concept generation is deferred to a later version.
Then stop.
Step 1 - Extract product identity anchors
Inspect the reference image and write a short internal identity lock:
- Product category and form factor.
- Shape and silhouette.
- Primary colors and materials.
- Logo, label, pattern, fasteners, ports, straps, handles, or other fixed details.
- Scale cues and proportions.
- What must not change.
Use these anchors in every generation prompt.
Step 2 - Build a three-slot shot plan
Create a compact shot plan before dispatch:
| Slot | Default aspect | Goal |
|---|---|---|
| main | 1:1 | Product-first marketplace image on white or soft neutral background |
| feature | 4:5 | One clear selling point with close-up detail or simple callout space |
| lifestyle | 4:5 | Realistic use context with the product still visually faithful |
If the project metadata provides imageAspect, use it when the user expects a
single aspect across the set. Otherwise use the slot defaults above.
Step 3 - Compose prompts with a fidelity lock
Every prompt must include this product fidelity instruction near the top:
Preserve the exact product identity from the reference image: shape,
silhouette, color, material, logo/label placement, visible construction
details, and proportions. Do not redesign the product. Do not add, remove,
or relocate product features.
Then add slot-specific instructions:
Main image prompt
- Product centered and fully visible.
- White, off-white, or very light grey background.
- Soft studio lighting with clean shadow.
- No props unless the user asked for them.
- No in-frame marketing text.
Feature image prompt
- Focus on one user-provided or safely inferred feature.
- Use close-up composition, cutaway-style crop, or clean negative space for later designer-added labels.
- Keep the product visually balanced in the frame. If no explicit callout structure is being generated, center the product. If label space is needed, offset the product only slightly and make the empty space feel intentional.
- Do not invent certifications, performance numbers, materials, or claims.
- Avoid tiny rendered text; leave label space instead.
Lifestyle image prompt
- Use a realistic environment matched to the product category.
- Keep the product the focal point.
- Show human interaction only if it helps explain use and does not obscure the product.
- Preserve product scale and structure.
Step 4 - Dispatch through the media contract
Use the unified Open Design media dispatcher. Do not call provider APIs or custom model commands directly.
For each slot, run the standard generate/wait loop:
# POSIX bash. Do not call provider APIs directly.
out=$("$OD_NODE_BIN" "$OD_BIN" media generate \
--project "$OD_PROJECT_ID" \
--surface image \
--model "<imageModel from metadata>" \
--aspect "<slot aspect or imageAspect from metadata>" \
--image "<project-relative product reference image>" \
--output "<product-slug>-<slot>.png" \
--prompt "<full slot prompt>")
ec=$?
if [ "$ec" -ne 0 ]; then echo "$out" >&2; exit "$ec"; fi
last=$(printf '%s\n' "$out" | tail -1)
task_id=$(printf '%s\n' "$last" |
python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('taskId',''))" 2>/dev/null)
since=$(printf '%s\n' "$last" |
python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('nextSince',0))" 2>/dev/null)
since="${since:-0}"
while [ -n "$task_id" ]; do
out=$("$OD_NODE_BIN" "$OD_BIN" media wait "$task_id" --since "$since")
ec=$?
last=$(printf '%s\n' "$out" | tail -1)
since=$(printf '%s\n' "$last" |
python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('nextSince',0))" 2>/dev/null)
since="${since:-0}"
if [ "$ec" -eq 0 ]; then
task_id=""
elif [ "$ec" -ne 2 ]; then
echo "$out" >&2
exit "$ec"
fi
done
printf '%s\n' "$last"
The final line must be JSON with {"file": {"name": "...", ...}}.
Record each final returned filename in image-manifest.json.
If the active image model or provider cannot use --image, stop and tell the
user that this workflow needs a reference-capable image generation path for
product fidelity.
Step 5 - Write image-manifest.json
After generation, create a project file named image-manifest.json:
{
"workflow": "ecommerce-image-workflow",
"mode": "reference-product",
"productName": "Example product",
"referenceImages": ["reference-product.png"],
"fidelityNotes": [
"Preserve product identity, color, material, construction, and proportions.",
"Do not treat these outputs as platform-compliance proof without human review."
],
"slots": [
{
"id": "main",
"role": "marketplace packshot",
"aspect": "1:1",
"output": "example-product-main.png",
"promptSummary": "Centered product-first packshot on a clean neutral background."
},
{
"id": "feature",
"role": "single feature highlight",
"aspect": "4:5",
"output": "example-product-feature.png",
"promptSummary": "Close-up or negative-space composition for one verified selling point."
},
{
"id": "lifestyle",
"role": "usage context",
"aspect": "4:5",
"output": "example-product-lifestyle.png",
"promptSummary": "Realistic scene with the product as the focal point."
}
]
}
Keep the manifest honest. If a detail is unknown, write null or a short note
instead of inventing claims.
Step 6 - Write ecommerce-gallery.html
Create a simple single-file HTML gallery that:
- Shows the reference image first.
- Shows the three generated slots with their role names.
- Lists product-fidelity notes.
- Links to
image-manifest.json. - Uses system fonts and local project files only; no CDN imports.
Step 7 - Hand off
Reply with:
- The generated filenames.
- A one-sentence summary of the fidelity lock used.
- A reminder that marketplace-specific compliance, final text overlays, and claim/legal review remain human review steps.
Do not emit an <artifact> tag.
Hard rules
- V1 requires real product reference imagery. No brief-only concept products.
- One product per run.
- Default to exactly three slots: main, feature, lifestyle.
- Preserve the product; do not redesign it.
- Do not invent claims, certifications, measurements, ingredients, or performance data.
- Use
"$OD_NODE_BIN" "$OD_BIN" media generate; do not call provider APIs directly. - Always create
image-manifest.jsonafter generation. - Run
references/checklist.mdbefore handoff.