brainstorm-experiments-existing

Design low-effort experiments to validate product assumptions with structured plans.

1|Updated Mar 23, 2026
One-click install
npx skills add https://github.com/cano721/ai-harness --skill brainstorm-experiments-existing-cano721
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: brainstorm-experiments-existing
Source: https://github.com/cano721/ai-harness/tree/main/teams/planning/bundle-claude/skills/brainstorm-experiments-existing
Command: npx skills add https://github.com/cano721/ai-harness --skill brainstorm-experiments-existing-cano721

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design low-effort experiments to validate assumptions for an existing product, enabling faster learning with minimal risk.

Core Features & Use Cases

  • Experiment ideation: Generate multiple experiment concepts for each key assumption (e.g., prototypes, fake doors, spikes).
  • Measurement planning: Define metrics, success criteria, and data collection methods for each experiment.
  • Structured outputs: Produce a markdown-ready plan detailing assumptions, experiments, metrics, and decision criteria.
  • Use Case: For a new feature idea, outline quick validation steps and a path to learning with minimal build.

Quick Start

Describe your feature idea and its key assumptions to generate a structured set of experiments.

Frequently Asked Questions about brainstorm-experiments-existing

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

FAQPage Schema
How do I plan low-effort experiments to validate assumptions for an existing product?

To validate assumptions for an existing product, you design low-effort experiments like prototypes, fake doors, or surveys. This approach enables faster learning with minimal risk by outlining quick validation steps before committing to development.

What is a fake door test and when should I use it for feature validation?

A fake door test is a low-effort experiment method to validate feature ideas cheaply before development. You should use it when you need to test user demand for a new feature concept with minimal build effort.

How do I define metrics and success criteria for A/B tests and prototypes?

You define metrics and success criteria by creating a structured measurement plan that outlines data collection methods and decision criteria for each experiment. This ensures clear evaluation paths for prototypes or A/B tests.

What's the best way to structure a product discovery experiment plan?

The best way to structure a product discovery experiment plan is to document assumptions, proposed experiments, metrics, and decision criteria in a markdown-ready format. This provides a clear path to learning with minimal build.

Can I use wizard-of-oz methods and spikes to test feature ideas cheaply?

Yes, you can use wizard-of-oz methods and spikes to test feature ideas cheaply. These low-effort experiment approaches allow you to validate assumptions and generate multiple concepts without committing to full development.