muapi-color-analysis-board skill
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.
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Install the muapi-color-analysis-board skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/SamurAIGPT/Generative-Media-Skills.git /tmp/Generative-Media-Skills mkdir -p ~/.claude/skills cp -r /tmp/Generative-Media-Skills/library/visual/color-analysis-board ~/.claude/skills/muapi-color-analysis-board
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
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
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:
- 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 mePresent 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 MUAPIAPIKEY 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/ -H "x-api-key: $MUAPIAPIKEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait .
- 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.
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