Pattern

Automates jewellery product marketing using Google Vertex AI (Gemini and Imagen) and Google Drive.

Pattern Jewellery Automation Skill

Overview

This skill automates the creation of high-end marketing content for Pattern Jewellery products. It orchestrates a sophisticated multi-agent pipeline: securely ingesting raw product photos, generating lifestyle and studio images via Imagen 3, writing SEO-optimized copy via Gemini 1.5 Pro, and systematically organizing the final assets in Google Drive.

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📥 Input Schema

The skill expects a trigger payload with the following fields:

• product_image (String): URL or base64 string of the raw product photograph.
• product_details (Object):
  • name: Product title (e.g. "Diamond Blue Sapphire Ring")
  • sku: Unique identifier (e.g. "R4389")
  • category: Organization category (e.g. "rings")
  • material: Composition (e.g. "18K white gold, 0.32ct diamond")
  • price_now: Current retail price (e.g. 4455)
  • description: Core design breakdown.

📤 Output Schema

• model_image_url: Link to the generated lifestyle model image.
• product_image_url: Link to the generated product-only image.
• caption: Formatted Instagram caption highlighting the luxury aesthetic.
• hashtags: Array of 20 optimized tags.
• drive_link: Public/Internal Google Drive folder URL hosting all generated assets.

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⚙️ Workflow Execution Steps

1. Vision & Prompt Generation (Gemini 1.5 Pro)

The system visually analyzes the product_image alongside the product_details to determine design intricacy, materials, and aesthetic quality. It then outputs two strictly constrained prompts:

• Model Prompt (Max 120 Tokens): A lifestyle photograph prompt targeting the Gulf luxury market. It details an elegant model wearing the piece in an upscale interior (e.g., modern Dubai), with specific studio lighting and bokeh settings.
• Product Prompt (Max 120 Tokens): A premium product-only photography prompt placing the piece on luxury backgrounds (e.g., white Carrara marble, deep navy velvet) equipped with three-point studio lighting and macro lens specs.

2. Parallel Image Generation (Imagen 3)

Using Google Vertex AI, this step dispatches parallel requests:

• Generates the model_image (1 sample, ultra quality, 4:5 aspect ratio, adult generation allowed).
• Generates the product_image (1 sample, ultra quality, 1:1 aspect ratio, tack-sharp).

3. Parallel Content Generation (Gemini 1.5 Pro)

Concurrently with the image rendering, the LLM drafts an engaging Instagram caption matching Pattern Jewellery's aspirational and traditional-modern fusion tone. It seamlessly integrates the price point and structural details, returning the copy alongside a 20-tag hashtag package.

4. Storage & Compilation (Google Drive)

All final image bytes (.jpg) and text output (.txt) are piped into the Google Drive API. They are systematically uploaded into a structured directory constraint: /Pattern_Jewellery/{category}/{sku}/.

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🧠 Memory Rules & State Management

This skill utilizes a persistent, 4-field memory map to iteratively improve generation over time based on user feedback. The core keys are:

• style: Default is "editorial"
• tone: Default is "aspirational-luxury"
• background_preference: Default is "white-marble"
• top_performing_caption: Cached high-performing copy for tone-matching. These variables dynamically inject into the prompt generation templates (Step 1).

⚡ Caching Protocol

To minimize unnecessary GPU execution costs:

1. Incoming images are hashed (SHA-256 or pHash).
2. Lookups occur against a Redis/Local cache mapping.
3. If the exact same image and metadata payload are received within the TTL window (30 days), the pipeline bypasses Gemini/Imagen entirely and immediately returns the cached Google Drive URL.

📂 Bundled Files

• jewellery_openclaw_skill.json: The core JSON pipeline graph mapped to OpenCLAW UI.
• jewellery_openclaw_skill.py: Background FastAPI worker capable of executing the pipeline outside of OpenCLAW.
• pattern_jewellery_openclaw_system.html: Front-end architectural diagram and design blueprint.