skills.rest
weisberg
weisbergCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

review-experiment

Audit A/B experiment code across seven review categories for correctness issues.

1|Updated Mar 19, 2026
Search Tags:#logging#code-review#assignment#feature-flag#ab-testing#bucketing
One-click install
npx skills add https://github.com/weisberg/agile_agentic_analytics --skill review-experiment
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: review-experiment
Source: https://github.com/weisberg/agile_agentic_analytics/tree/main/plugins/ab-testing/skills/review-experiment
Command: npx skills add https://github.com/weisberg/agile_agentic_analytics --skill review-experiment

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Review A/B experiment implementation code to identify assignment bugs, bucketing leaks, and logging or feature-flag issues.

Core Features & Use Cases

  • Assignment Correctness
  • Bucketing Stability
  • Exposure Logging
  • Metric Instrumentation
  • Feature Flag Hygiene
  • Performance
  • Cleanup and Interactions

Quick Start

Analyze the provided experiment code or configuration file and enumerate issues across the seven review categories, citing exact lines where relevant.

Frequently Asked Questions about review-experiment

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit A/B testing code for assignment bugs and bucketing leaks?▼

Review A/B experiment code by analyzing assignment logic, bucketing stability, and exposure logging across code paths. Enumerate issues across seven categories including feature flag hygiene and metric instrumentation, citing exact lines to guide remediation.

What common edge cases cause exposure logging and feature flag issues in experiment implementations?▼

Exposure logging and feature flag issues often stem from cookie lifecycles, race conditions, and inconsistent evaluations across SDKs. Auditing experiment code targets these edge cases by checking metric instrumentation and assignment correctness to ensure safe deployments.

How do I review experiment code to check bucketing stability and metric instrumentation?▼

Review experiment code by applying seven defined categories covering bucketing stability, metric instrumentation, and feature flag hygiene. Analyze the provided codebase context to catch edge cases and enumerate specific findings referencing exact lines for remediation.

Can I audit A/B test configurations across different SDKs and deployment configurations?▼

Yes, you can audit A/B test configurations across SDKs and deployment configurations. The review evaluates assignment correctness, exposure logging, and feature flag usage across code paths to catch inconsistent evaluations and race conditions.

What do I need to provide to audit A/B test correctness and safety in my codebase?▼

To audit A/B test correctness and safety, you must provide the codebase context or specific files to analyze. The review applies seven categories covering assignment, bucketing, exposure logging, and performance to identify issues with exact line references.

Related Skills

Explore More Software Engineering Skills→
doodledooddoodledood
17

consultant

Run asynchronous code analyses with LiteLLM-powered AI models.

Community
Intermediate
shekerkammashekerkamma

tracking-gaps

Identify missing analytics data points and generate instrumentation requests.

Community
Basic
BoraPerusicBoraPerusic

data-engineering-data-driven-feature

Guide data-driven feature development with A/B testing and continuous measurement.

Community
Advanced
juandaniel190juandaniel190
1

statistical-analysis

Apply descriptive statistics, trend analysis, outlier detection, and hypothesis testing to datasets using Python libraries for calculations and visualizations.

Community
Intermediate