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genpark-agentic-multimodal-wardrobe-outfit-curator-skill

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SUMMARY

AI Multimodal Wardrobe & Capsule Outfit Curator. Aligns digital wardrobe inventories with real-time weather forecasts, occasion formality constraints, and color harmony models (monochromatic, complementary, neutral anchors) to curate optima | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-agentic-multimodal-wardrobe-outfit-curator-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

AI Multimodal Wardrobe & Capsule Outfit Curator. Aligns digital wardrobe inventories with real-time weather forecasts, occasion formality constraints, and color harmony models (monochromatic, complementary, neutral anchors) to curate optimal daily looks and recommend missing complementary items for seamless one-click commerce checkout.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import AgenticMultimodalWardrobeOutfitCurator

def main():
    curator = AgenticMultimodalWardrobeOutfitCurator()
    sample_wardrobe = [
        {"id": "w1", "name": "Oversized Linen Shirt", "category": "top", "type": "t-shirt", "color": "White"},
        {"id": "w2", "name": "Pleated Dress Trousers", "category": "bottom", "type": "trousers", "color": "Charcoal"},
        {"id": "w3", "name": "Derby Shoes", "category": "footwear", "type": "derbies", "color": "Black"}
    ]
    ctx = {"temp_c": 16.0, "precip_prob": 0.10, "occasion": "gallery_opening"}
    result = curator.curate_outfit(sample_wardrobe, ctx)
    print("--- GenPark Curated Outfit Output ---")
    print(f"Occasion: {result['occasion']} | Ambient Temp: {result['ambient_temp_c']}C")
    print(f"Harmony: {result['color_harmony']['style_palette']} (Score: {result['color_harmony']['harmony_score']})")
    print(f"Verdict: {result['readiness_verdict']}")
    for item in result['outfit_items']:
        print(f"  - [{item['category'].upper()}] {item['name']} ({item['color']})")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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