genpark-generative-seo-skill
GenPark AI Agent Skill -- # Generative Engine SEO (GEO) Optimizer Skill
This repository contains the Generative Engine SEO (GEO) Optimizer Skill — a modular developer Python client SDK, agent skill interface configuration (skill.json), and executable workflow tests. It is designed to restructure e-commerce catalog information to maximize recommendation and citation probability on AI-driven search platforms (e.g., Perplexity AI, ChatGPT Search, Gemini).
🚀 Capabilities
- AI Citability Scoring: Evaluates product listing text against heuristics preferred by LLM crawlers (e.g. data density, specifications depth, comparative phrases).
- Information Density Restructuring: Rewrites typical marketing copy ("fluff") into structured, comparative descriptions with clear attribute specifications.
- JSON-LD Schema Generation: Instantly outputs valid Product structured schemas for indexing by search crawlers.
🛠️ Setup & Installation
Install dependencies:
pip install -r requirements.txtConfiguration: Set your API environment variables if executing requests against the live production server (otherwise, client executes in mock mode):
- PowerShell:
$env:GEO_API_KEY="your_api_key" - bash:
export GEO_API_KEY="your_api_key"
- PowerShell:
💻 SDK Usage Reference
from generative_seo import GenerativeSeoClient
# Initialize Client (mock mode by default)
client = GenerativeSeoClient()
# Audit content for AI search indexability
audit = client.analyze_citability(
content="This is the most amazing widget ever made in the universe.",
specs={"weight": "150g", "battery": "500mAh"}
)
print(f"Citability: {audit['citability_score']}")
# Optimize listing content
optimized = client.optimize_content(
product_name="Zenith Earbuds",
original_copy="Amazing earbuds.",
specs={"battery": "32h", "bt_version": "5.3"}
)
print(optimized["optimized_text"])
print(optimized["schema_markup"])
📜 License
This project is licensed under the MIT License.