Pattern

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

Install

openclaw skills install @itsmebasil/pattern-skills

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.