scenario-product-shots
Scenario Product Shots
Overview
The product is never generated. A text-prompted bottle ships a wrong label to a landing page; every credible shot starts from an uploaded photo of the real product, and fidelity is a gated check, not a hope. Two lanes cover most work: deterministic packshot tools (cutout, background, shadow, relight) and generative scene placement with an instruction-editing model. Connection and the core loop: see the scenario skill. Edit-model contracts: scenario-image. Deterministic tools: scenario-image-editing. Reading assets back: scenario-asset-analysis. Animating an approved still into an ad: scenario-video-ads. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.
Quick reference
| Need | Route |
|---|---|
| The source | upload_asset the best photo available: sharp, evenly lit, whole product in frame (search "uncrop" finds tools that rebuild a clipped edge) |
| Fidelity checklist | asset_analyze the upload once, instructing an inventory of label text, geometry, materials, and colors; every later check reuses it |
| Packshot | recommend with the packshot need in the user's own words (a cutout-and-stage tool with solid, transparent, or custom backgrounds, margins, and shadows was the authoring-time hit), or background removal plus your own compose |
| Lifestyle scene | recommend with capability="img2img", product photo as reference, preserve-first prompt, one scene per run |
| Relight | search "relighting" (the authoring-time hit adjusts light, exposure, and mood, with a brand-color lock) |
| Upscale keepers | recommend with the user's own words; product-tuned upscalers existed at authoring time |
| Gate | asset_analyze the outputs against the checklist: up to 10 ids in images, the saved checklist in text_inputs, the letter-by-letter brief in instruction |