genpark-refund-policy-optimizer-skill

GenPark AI Agent Skill — Analyze refund patterns and optimize e-commerce refund policies for retention, cost, or balance.

Features

• Refund pattern analysis: top reasons, approval rates, preventable returns
• Policy customer-friendliness scoring (0-100)
• Three optimization goals: maximize_retention, minimize_costs, balanced
• Projected impact on retention, conversion, and costs
• Actionable recommendations

Quick Start

from client import RefundPolicyClient

client = RefundPolicyClient()
result = client.optimize(
    refund_records=[{"amount": 45.0, "reason": "wrong size", "days_since_purchase": 12, "approved": True}],
    current_policy={"return_window_days": 21, "restocking_fee_pct": 15, "free_returns": False},
    business_goal="balanced",
)
print(f"Policy Score: {result['policy_score']}/100")
print(result["optimized_policy"])

Installation

python example_usage.py  # No external dependencies

Built by GenPark | alphaparkinc