Cinematic Product Ad

Cinematic 5–10s product ad from a product photo + brand brief.

Inputs

Steps

This skill has TWO phases separated by a user pick. Submit them as two separate the plan calls — never bundle downstream steps into the first plan.

Phase A — variant exploration (cheap)

Submit ONE the plan containing only:

1. Hero frame variants — 4 separate muapi image generate nodes (model=nano-banana-2, aspect_ratio=16:9 by default).
   • Each prompt restyles the product against the brand brief mood. Vary lighting, palette, framing, and lens between variants. Keep product geometry intact.
   • Reference the user's product_image if the model supports image conditioning; otherwise describe the product in detail.

After the plan executes, end your turn with a brief message listing the 4 asset_ids and asking the user which one to take forward (e.g. "Pick a hero (asset_1, asset_2, asset_3, or asset_4)?"). Wait.

Phase B — commit on the picked hero (expensive)

Once the user replies with their pick, submit a SECOND the plan:

1. Upscale the picked frame — enhance_image (operation=upscale).
2. Animate the upscaled frame — muapi video from-image (model=kling-v3.0-standard-image-to-video, duration={{duration_sec}}, prompt="slow cinematic push-in, soft volumetric light, subtle product micro-rotation"). Reference the upscale's URL with $nX.url.
3. Background music — muapi audio create (kind=music) — runs in parallel with the upscale/animate. Style derived from brand_brief (luxury → "ambient cinematic, warm strings, slow tempo, instrumental"). Duration ≈ video length.
4. Return the upscaled hero image and the final video.

Notes

• If the brief mentions "luxury", bias the palette to gold/black; for "playful", bias to bright/saturated.
• If video gen fails after failover, fall back to a still-frame slideshow (just return the upscaled hero + music).
• Don't auto-confirm step 4 — its cost (~80 cr) deserves a user nod.

Trigger Keywords

product ad, commercial, cinematic ad, product video

Notes for the Executing Agent

• This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset.
• For model IDs without a CLI alias yet, fall back to the raw endpoint via curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id>.
• Substitute {{input_name}} placeholders with the user's actual inputs before issuing each call.