prediction-hypothesis-engine

Manages hypothesis-driven experiments using the Pendulum Framework.

Updated Mar 30, 2026

Search Tags:#product development #experimentation #hypothesis tracking #data-driven decisions #iteration speed

Ready to run

npx skills add https://github.com/GGsLATAM/atomic-scaling-os --skill prediction-hypothesis-engine

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prediction-hypothesis-engine
Source: https://github.com/GGsLATAM/atomic-scaling-os/tree/main/.claude/plugins/atomic-scaling-os/skills/prediction-hypothesis-engine
Command: npx skills add https://github.com/GGsLATAM/atomic-scaling-os --skill prediction-hypothesis-engine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of managing and iterating on experiments, ensuring faster decision-making and learning cycles for rapid product development.

Core Features & Use Cases

Hypothesis Tracking: Systematically create, track, and analyze hypotheses against actual outcomes.
Pendulum Framework Integration: Utilize a two-column tracker for Hypothesis and Measured results.
Cycle Management: Execute the Hypothesis→Measure→Change cycle for continuous improvement.
Threshold-Based Decision Making: Set clear kill thresholds to rapidly pivot or continue experiments.
Data-Driven Decisions: Support data-driven decisions by tracking key metrics and learning outcomes.
Use Case: For a SaaS product, quickly iterate on feature improvements by setting hypotheses, measuring outcomes, and making informed decisions based on data.

Quick Start

Activate the prediction-hypothesis-engine skill by mentioning "Experiments" or "hypotheses" and trigger it with the /prediction-hypothesis-engine command.

Frequently Asked Questions about prediction-hypothesis-engine

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

FAQPage Schema

How do I track hypothesis-driven experiments for product development?▼

Hypothesis-driven experiments are tracked using a two-column Pendulum Framework tracker, mapping hypotheses against measured results to support data-driven decisions. This cycle management approach enables rapid iteration and continuous feature refinement for software products.

What is the Hypothesis Measure Change cycle for rapid iteration?▼

The Hypothesis Measure Change cycle is a continuous improvement loop for rapid product development. You generate a hypothesis, measure actual outcomes against it, and make data-driven decisions to change or pivot features based on predefined kill thresholds.

How do I set kill thresholds for feature experimentation?▼

Kill thresholds for feature experimentation are set to establish clear decision points for rapidly pivoting or continuing experiments. This threshold-based decision making supports data-driven product development by defining when to halt iterations based on measured results.

Can I use this Pendulum Framework approach for SaaS feature refinement?▼

Yes, the Pendulum Framework is explicitly designed for SaaS product scenarios to quickly iterate on feature improvements. It systematically tracks hypotheses and measured outcomes to enable data-driven decisions for rapid iteration and learning velocity.

What is the best way to manage data-driven decisions in software products?▼

Data-driven decisions in software products are best managed by systematically tracking key metrics and learning outcomes against hypotheses. Utilizing a structured framework ensures faster decision-making and rapid iteration cycles throughout the experimentation process.