genpark-referral-program-skill
GenPark AI Skill - Design and simulate customer referral programs with viral coefficient K-factor and 12-month ROI projections
Install / Use
claude mcp add alphaparkinc -- npx -y github:alphaparkinc/genpark-referral-program-skillIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AutomationSupported Platforms
Skill content
View source on GitHubgenpark-referral-program-skill
GenPark AI Agent Skill -- Design, simulate, and optimize customer referral programs with viral coefficient and ROI projections.
Features
- Viral coefficient (K-factor) calculation
- K-factor grading: Weak / Moderate / Strong / Viral
- Reward cost modeling: cash (100%) vs credit (60%) vs discount (40%)
- 12-month customer growth simulation
- CAC comparison: referral vs standard acquisition
- Full ROI projection
- Program design recommendations
Quick Start
from client import ReferralProgramClient
client = ReferralProgramClient()
result = client.design(
avg_order_value=65,
customer_acquisition_cost=28,
referrer_reward={"type": "credit", "value": 15},
referee_reward={"type": "discount", "value": 20},
current_customers=2500,
)
print(f"K-factor: {result['viral_coefficient']} ({result['k_factor_grade']})")
print(f"12m new customers: {result['program_roi']['total_new_customers_12m']}")
Installation
python example_usage.py # No external dependencies
Built by GenPark | alphaparkinc
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