genpark-checkout-ab-test-skill
GenPark AI Skill - Design, simulate, and analyze A/B tests for e-commerce checkout flows with statistical significance testing
Install / Use
claude mcp add alphaparkinc -- npx -y github:alphaparkinc/genpark-checkout-ab-test-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
Development & EngineeringSupported Platforms
Our assessment of genpark-checkout-ab-test-skill
genpark-checkout-ab-test-skill scores 57/100 on our quality scale, 4134th of 4,607 Development & Engineering skills we index.
Its MCP Server is 1019 bytes long, split into 4 sections with 2 code examples: fairly brief, so expect to fill in details yourself.
It has 9 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 3 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
- Our last check on 2026-09-01 found the source still online.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 78/100, with 2 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-30. Automated pattern scan on 2026-09-30. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
genpark-checkout-ab-test-skill compared with similar skills
All 4 of these similar skills score higher than genpark-checkout-ab-test-skill; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| genpark-checkout-ab-test-skill (this skill)by alphaparkinc | 57 | 9 | 3mo ago | MCP Server |
| Agent-Reachby Panniantong | 100 | 98.5k | 2d ago | CLAUDE.md |
| taste-skillby Leonxlnx | 100 | 98.2k | 3d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 75.6k | today | CLAUDE.md |
| ai-job-searchby MadsLorentzen | 100 | 49.3k | 1d ago | CLAUDE.md |
Frequently asked questions
- How do I install genpark-checkout-ab-test-skill?
- Run
claude mcp add alphaparkinc -- npx -y github:alphaparkinc/genpark-checkout-ab-test-skill. The install tabs above show the steps for each supported agent. - Which AI agents does genpark-checkout-ab-test-skill work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is genpark-checkout-ab-test-skill safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 78/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is genpark-checkout-ab-test-skill still maintained?
- The repository was last updated about 3 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
Skill content
View source on GitHubgenpark-checkout-ab-test-skill
GenPark AI Agent Skill — Design, simulate, and analyze A/B tests for e-commerce checkout flows.
Features
- Two-proportion z-test for statistical significance
- Conversion lift and revenue impact calculation
- Statistical power estimation
- Sample size calculator (test design)
- Monte Carlo simulation for false positive rates
- Clear Ship / Continue / Revert recommendations
Quick Start
from client import ABTestClient
client = ABTestClient()
result = client.analyze(
control={"visitors": 10000, "conversions": 300, "avg_order_value": 80},
variant={"visitors": 10000, "conversions": 340, "avg_order_value": 82},
confidence_level=0.95,
monthly_traffic=40000,
)
print(result["recommendation"])
print(f"Revenue impact: ${result['revenue_impact_usd']:,.2f}/month")
Installation
python example_usage.py # No external dependencies
Built by GenPark | alphaparkinc
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.