Estimated credits: ~24 per run.

1. For each requested aspect ratio in

   {{formats}}

   :
   • Call

     muapi image edit

     (model=nano-banana-2-edit) with the source image.
   • Prompt: "Reframe the composition to {{aspect_ratio}} for social media. Keep the subject centred and uncropped. Match the original lighting and palette. {{caption_idea_hint}}".
   • Where

     {{caption_idea_hint}}

     = "Leave headroom at the top for a caption: '<caption_idea>'." (only if caption_idea is non-empty).
2. All formats run in parallel (no inter-node dependencies).
3. Return one asset id per format, labelled with the platform it suits:
   • 1:1 → Instagram feed
   • 9:16 → TikTok / IG Reels / YT Shorts
   • 4:5 → Instagram portrait
   • 16:9 → Twitter / X / YouTube
   • 21:9 → cinematic banner

• Don't generate fresh images — only reframe / re-edit the source.
• If the source already matches a format, skip that node and surface the original asset id for it.

• 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.