genpark-flash-sale-optimizer-skill
GenPark AI Skill - Optimize flash sale timing, discount depth, inventory allocation and urgency messaging for maximum revenue
Install / Use
claude mcp add alphaparkinc -- npx -y github:alphaparkinc/genpark-flash-sale-optimizer-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-flash-sale-optimizer-skill
GenPark AI Agent Skill -- Optimize flash sale timing, discount depth, inventory allocation, and urgency messaging.
Features
- Margin-aware optimal discount calculation per product
- 70% inventory cap rule to protect base stock
- Day/time timing recommendations with engagement scores
- Conversion rate estimation based on discount depth and urgency
- 7 urgency message templates with dynamic variable injection
- Revenue projection against target
Quick Start
from client import FlashSaleClient
client = FlashSaleClient()
result = client.optimize(
products=[{"name": "Serum", "regular_price": 35, "cost": 7, "current_stock": 200}],
sale_duration_hours=24,
audience_size=20000,
)
print(result["sale_plan"])
print(result["urgency_messages"])
Installation
python example_usage.py # No external dependencies
Built by GenPark | alphaparkinc
Related Skills
Agent-Reach
78.4kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
ruflo
71.0k🌊 The original agent meta-harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
career-ops
70.3kOpen-source AI job search: scan job portals, evaluate listings into a structured A-H report with a global 1-5 score, tailor your CV, track applications — runs locally in your AI coding CLI (Claude Code, Codex, OpenCode, Antigravity…)
headroom
69.1kCompress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
