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genpark-personal-agent-subscription-stealth-price-hike-detector-skill

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Python
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genpark-personal-agent-subscription-stealth-price-hike-detector-skill

Official Hub LinkedIn Zero External Dependencies MCP Compliant License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Personal Consumer SaaS & Streaming Stealth Price Hike Detector. Audits recurring bank/card subscription charges, detects sneaky fee increases or expired trial conversions, calculates annualized budget leakages, and generates retention negotiation dispute letters.

🌐 Official GenPark AI Ecosystem Links

Related MCP server: Subscription Sniper — AI Agent for Recurring Spend Audits

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.

  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.

  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.

  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import PersonalAgentSubscriptionPriceHikeDetector

def main():
    detector = PersonalAgentSubscriptionPriceHikeDetector()
    items = [
        {"service_name": "DevAI Copilot", "billing_cycle": "monthly", "baseline_price": 20.0, "current_price": 29.0},
        {"service_name": "Gym Pass", "billing_cycle": "monthly", "baseline_price": 50.0, "current_price": 65.0}
    ]
    res = detector.audit_subscription_charges(items)
    print("--- Subscription Hike Audit ---")
    print(f"Hikes Detected: {res['hikes_detected_count']} | Total Annual Leakage: ${res['total_annualized_leakage_usd']}")
    for f in res['flagged_subscriptions']:
        print(f"* {f['service_name']}: +${f['hike_amount']} (+{f['hike_percentage']}%)")
        print(f"  Negotiation Script: {f['dispute_negotiation_script'][:80]}...")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI. Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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