experiment

Name: experiment
Availability: InStock
Author: titta

Track structured experiment hypotheses, variants, and results in a SQLite database.

29|12|Updated Mar 30, 2026

Search Tags:#optimization #learning #strategy #sqlite #experimentation #gtm

Ready to run

npx skills add https://github.com/matteotitta/genesys-skills --skill experiment-matteotitta

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment
Source: https://github.com/matteotitta/genesys-skills/tree/main/skills/meta/learning/experiment
Command: npx skills add https://github.com/matteotitta/genesys-skills --skill experiment-matteotitta

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the problem of fragmented knowledge and repetitive failure in GTM strategy by providing a centralized, searchable database to track hypotheses, variants, and outcomes.

Core Features & Use Cases

Structured Experiment Tracking: Logs hypotheses, controlled dimensions, and results in a SQLite database to prevent re-testing failed tactics.
Learning Promotion: Automatically identifies durable learnings that can be promoted to permanent system rules or client-specific documentation.
Use Case: When testing new LinkedIn hooks, use this skill to log the performance of different variants, ensuring that future content generation is informed by past data rather than guesswork.

Quick Start

Invoke the experiment skill to start a new test by typing /experiment new followed by a description of your hypothesis.

Frequently Asked Questions about experiment

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

FAQPage Schema

How do I track GTM experiments and prevent testing the same failed messaging twice?▼

Track GTM experiments by logging hypotheses, variants, and outcomes in a centralized SQLite database. This prevents redundant testing of failed tactics and compounds strategic knowledge for future content and messaging iterations.

What is the best way to log content messaging variants for structured experimentation?▼

Log content messaging variants by recording hypotheses and controlled dimensions within a SQLite database. This structured experimentation approach maintains persistent records of results, preventing repetitive failure and promoting durable learnings for future workflows.

Do I need a SQLite database to manage the lifecycle of GTM hypotheses and results?▼

Yes, SQLite integration is required to maintain persistent records of experimental data and learning outcomes. The database manages the full lifecycle of hypotheses and variants, preventing fragmented knowledge across your go-to-market strategy.

How do I promote successful experiment learnings into permanent system rules?▼

Promote successful experiment learnings by automatically identifying durable outcomes from your tracked variants. The system promotes these durable learnings to permanent rules or client-specific documentation, ensuring future content generation is informed by past data.

Can I use structured experiment tracking for LinkedIn hooks and content iterations?▼

Yes, use structured experiment tracking for LinkedIn hooks by logging the performance of different variants. This ensures future content generation is informed by past data within your SQLite database rather than guesswork.

Why does fragmented knowledge cause repetitive failure in GTM strategy optimization?▼

Fragmented knowledge causes repetitive failure because past experiment outcomes are not centrally stored. Structured testing in a searchable SQLite database prevents this by tracking hypotheses and results, compounding strategic knowledge for ongoing optimization.