slug: muapi-color-analysis-board name: muapi-color-analysis-board version: "1.0.0" description: Turn a portrait photo into a high-end editorial "Color Analysis Board" in a luxury fashion-magazine style (Dior / Ralph Lauren aesthetic) — best colors, undertone, makeup guide, capsule wardrobe, hair & jewelry recommendations, all laid out on a clean beige/ivory grid. acceptLicenseTerms: true

Color Analysis Board

Turn a portrait photo into a high-end editorial "Color Analysis Board" in a luxury fashion-magazine style (Dior / Ralph Lauren aesthetic) — best colors, undertone, makeup guide, capsule wardrobe, hair & jewelry recommendations, all laid out on a clean beige/ivory grid.

Inputs

| Name | Type | Required | Default | Description | |:---|:---|:---|:---|:---| | person_image | image_url | yes | — | A clear, well-lit portrait of the person. Front-facing, neutral background, and natural lighting give the strongest color reads (undertone, hair, eyes). Avoid heavy filters or makeup that masks the natural complexion. |

Steps

Phase A — Color Analysis Board Generation

If {{person_image}} is not provided, ask the user to upload a clear front-facing portrait. Make sure the face is well-lit with natural color (no heavy filters, color-cast lighting, or sunglasses) — the model needs accurate skin, hair, and eye color to pick the right palette.

Once the photo is available, submit ONE step to generate the color analysis board:

1. Color Analysis Board Generation — muapi image edit (model=gpt-image-2-image-to-image):
   • Reference Image: {{person_image}}
   • Image size: 3840x2160 (16:9 landscape) — magazine-spread aspect ratio
   • Background: auto
   • Output format: png
   • Quality: auto
   • Moderation: low
   • Prompt:

     Create a high-end editorial "Color Analysis Board" from this portrait in a luxury fashion magazine style (Dior / Ralph Lauren aesthetic). Clean beige/ivory background, warm tones, soft diffused lighting, ultra-detailed photorealistic quality, consistent lighting, minimal elegant typography, grid-based layout.
     
     Main portrait: enhanced natural beauty (same identity, smooth skin, soft glow, realistic texture)
     Top section: "Your Best Colors" with fabric swatches with the best algorithm choices
     Undertone panel: warm / neutral / cool with marked result.
     Colors to avoid
     Neutrals that work
     Prints that flatter
     Makeup guide: eyeshadows, blush, lips, highlighter
     "You in your colors": multiple outfit best variations
     Hair colors: best.
     Jewelry
     Style notes
     
     Capsule wardrobe: coordinated outfits, shoes, bags, accessories
     Style: best style for me
     

Present the generated board to the user. Suggest variations they can try: a different source portrait (different lighting / hairstyle for comparison), or asking to bias the palette toward a season (e.g. "spring warm" vs "winter cool") or a specific brand aesthetic (e.g. minimalist Scandinavian, Old Money, streetwear).

Trigger Keywords

color analysis, color analysis board, personal color palette, seasonal color analysis, undertone analysis, style guide board, fashion color board, capsule wardrobe board

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.
• Source schema reference: gpt-image-v2-edit (from the source workflow JSON) maps to gpt-image-2-image-to-image in the muapi catalog.
• The output is intentionally 16:9 (3840×2160) so it reads as a magazine spread / desktop wallpaper / Pinterest landscape board. For IG-feed square or 9:16 vertical, request a re-crop or re-run with a different image_size.