Operating context
- At least one uploaded product reference image in the active project.
- Product name or short label if it is not obvious.
- Main selling point if the feature image cannot be inferred safely.
nexu-io/open-design
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.
npx skills add https://github.com/nexu-io/open-design --skill "skills/ecommerce-image-workflow"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
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 c…
npx skills add https://github.com/nexu-io/open-design --skill "skills/ecommerce-image-workflow"The pinned source contains enough sections and task detail for a source-grounded deep guide; automated content is still not an independent test.
1,147 source words · 17 usable sections
Operating context
Operations workflow
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
Before planning, verify that the current project includes a real product reference image.
Before planning, verify that the current project includes a real product reference image.
Inspect the reference image and write a short internal identity lock:
Create a compact shot plan before dispatch:
Every prompt must include this product fidelity instruction near the top:
SkillSignal prompt templates
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Source-grounded prompt
Use for a operations or automation task while explicitly checking the source sections.
Use ecommerce-image-workflow for this operations or automation task: [task]. Inputs and constraints: [details]. Work through these pinned SKILL.md sections: “Workflow”, “Step 0 - Confirm reference-product mode”, “Step 1 - Extract product identity anchors”, “Step 2 - Build a three-slot shot plan”, “Step 3 - Compose prompts with a fidelity lock”. Cite the concrete requirements that shape each step, do not invent capabilities absent from the source, and verify the result against: [acceptance criteria].
Operations checklist
The source section “Workflow” has been checked.
The source section “Step 0 - Confirm reference-product mode” has been checked.
The source section “Step 1 - Extract product identity anchors” has been checked.
The source section “Step 2 - Build a three-slot shot plan” has been checked.
Static permission evidence
These are source excerpts matched by deterministic rules, not findings of malicious behavior, safety, or actual execution.
SKILL.md · L156
python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('taskId',''))" 2>/dev/null)The documentation asks the agent to run terminal commands or scripts.
SKILL.md · L158
python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('nextSince',0))" 2>/dev/null)The documentation asks the agent to run terminal commands or scripts.
SKILL.md · L188
After generation, create a project file named `image-manifest.json`:The documentation asks the agent to create, modify, or delete local files.
SKILL.md · L231
Create a simple single-file HTML gallery that:The documentation asks the agent to create, modify, or delete local files.
Choose a different workflow
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
A separate implementation from event4u-app/agent-config; compare its source, maintenance signals, and permission requirements.
Open source detailDistributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailMedicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.
A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
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 c…
The source record exposes this install command: npx skills add https://github.com/nexu-io/open-design --skill "skills/ecommerce-image-workflow". Inspect the command and pinned source before running it.
Static rules flagged exec-script, write-files in the source; the page lists the matching lines and excerpts.
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Use when the user wants to generate an image or video via Higgsfield AI. Covers 30+ models: Soul V2, Seedance 2.0, Kling 3.0, Veo 3.1, GPT Image 2, Nano Banana 2. Also covers Marketing Studio — branded ad video/image with avatars and products. Use whenever: "generate an image", "make a video", "animate this photo", "image-to-video", "img2vid", "edit this image with AI", "produce a clip", "create an ad", "make a UGC video", "marketing video", "brand video", "TV spot", "import product from URL", "
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.
ecommerce-image-workflow/
|-- SKILL.md
|-- example.html
`-- references/
`-- checklist.md
By default, generate three ecommerce-ready image assets for one product:
Also create:
image-manifest.json describing reference inputs, slots, prompts, outputs,
aspect ratios, and fidelity notes.ecommerce-gallery.html as a small preview gallery linking the generated
files and summarizing the image roles.Required:
Ask only for missing essentials:
Do not ask broad discovery questions. Keep the workflow moving.
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.
Inspect the reference image and write a short internal identity lock:
Use these anchors in every generation prompt.
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.
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:
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.
image-manifest.jsonAfter 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.
ecommerce-gallery.htmlCreate a simple single-file HTML gallery that:
image-manifest.json.Reply with:
Do not emit an <artifact> tag.
"$OD_NODE_BIN" "$OD_BIN" media generate; do not call provider APIs
directly.image-manifest.json after generation.references/checklist.md before handoff.