genpark-product-comparison-agent-skill
GenPark multi-product comparison agent skill. Generates structured side-by-side spec markdown tables and weighted scores.
Install / Use
claude mcp add alphaparkinc -- npx -y github:alphaparkinc/genpark-product-comparison-agent-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-product-comparison-agent-skill
This repository contains the GenPark Product Comparison Agent Skill — an agent configuration skill config (skill.json), a production-ready Python SDK client (comparison_agent.py), and executable verification tests. It is designed to compare multiple retail/product SKUs, apply dynamic weights to user pricing vs. features priorities, and generate structured markdown tables.
🚀 Capabilities
- Weighted Priority Scoring: Calculates relative values based on custom importance factors.
- Auto-Markdown Generation: Automatically constructs side-by-side spec comparison matrices.
- Verdict Synthesis: Resolves standard action guidelines for product recommendations.
🛠️ Setup & Installation
- Install dependencies:
pip install -r requirements.txt
💻 SDK Usage Reference
from comparison_agent import ProductComparisonClient
# Initialize client
client = ProductComparisonClient()
# Execute comparison
result = client.compare(
products=[
{"sku": "SKU-A", "name": "Item A", "price": 100, "rating": 4.5, "features": {"f1": "yes"}},
{"sku": "SKU-B", "name": "Item B", "price": 80, "rating": 4.0, "features": {}}
],
priorities={"price_importance": 4, "feature_richness": 1}
)
print(result["comparison_table_markdown"])
📜 License
This project is licensed under the MIT License.
Related Skills
Agent-Reach
80.6kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
ruflo
72.4k🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
headroom
72.0kCompress 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.
career-ops
71.5kOpen-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…)
Languages
Security Score
Audited on Jul 10, 2026
