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experiment-workspace

Create, update, and archive database-backed experiments with Bayesian A/B testing.

1|Updated Mar 16, 2026
Search Tags:#A/B testing#experiment#experiment tracking#Bayesian analysis#bandit algorithms
One-click install
npx skills add https://github.com/featbit/featbit-release-decision-agent --skill experiment-workspace
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: experiment-workspace
Source: https://github.com/featbit/featbit-release-decision-agent/tree/main/skills/experiment-workspace
Command: npx skills add https://github.com/featbit/featbit-release-decision-agent --skill experiment-workspace

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires project-sync, scripts/analyze.ts, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill centralizes the creation, analysis, and management of experiments by replacing typical dashboard workflows with database-backed records, enabling seamless tracking and decision-making.

Core Features & Use Cases

  • Experiment Management: Automates creation, updating, and archiving of experiments directly in the database.
  • Analysis Orchestration: Triggers server-side Bayesian or bandit analyses, integrating results into the workflow.
  • Use Case: A product team wants to run an A/B test, analyze data with Bayesian methods, and automatically plan the next steps based on the outcome, all within their codebase.

Quick Start

Use this Skill to create and analyze a new experiment by providing the flag details and desired metrics.

Frequently Asked Questions about experiment-workspace

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

FAQPage Schema
How do I track A/B testing experiments directly within a database environment?▼

You can track A/B testing experiments in a relational database by using an integrated system that replaces dashboard workflows with database-backed records. This enables seamless experiment lifecycle management from setup through analysis and closure.

What is the best way to run Bayesian analysis for product experiments without leaving my codebase?▼

The best way to run Bayesian analysis within your codebase is to use an integrated experiment management system that triggers server-side analysis endpoints. This orchestrates Bayesian A/B testing and automatically plans next steps based on the outcome.

How do I manage the full experiment lifecycle from setup to closure automatically?▼

You manage the full experiment lifecycle by defining, tracking, and analyzing experiments through a database-backed system. This ensures data integrity and decision traceability from initial setup through analysis to final closure.

Do I need a relational database to use bandit algorithms for experiment tracking?▼

Yes, you need synchronization with a relational database and server-side analysis endpoints to effectively use bandit algorithms for experiment tracking. This setup ensures data integrity and decision traceability for your experiments.

When should I not use a database-backed workflow for A/B testing?▼

You should avoid a database-backed workflow for A/B testing if you lack a relational database or server-side analysis endpoints. This system requires synchronization with these components to function effectively and ensure data integrity.

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