- name
- Pattern Jewellery Automation
- description
- 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.
📥 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.
⚙️ 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}/.
🧠 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:
- Incoming images are hashed (SHA-256 or pHash).
- Lookups occur against a Redis/Local cache mapping.
- 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.