skills.rest
tom-xs
tom-xsCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

define-hypothesis

Structure assumptions into falsifiable hypotheses with success metrics and validation approaches.

Updated Nov 22, 2025
Search Tags:#validation#metrics#lean startup#hypothesis#product discovery#experimentation
One-click install
npx skills add https://github.com/tom-xs/nixos-dotfiles --skill define-hypothesis-tom-xs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: define-hypothesis
Source: https://github.com/tom-xs/nixos-dotfiles/tree/main/ai/kimi-skills/skills/define-hypothesis
Command: npx skills add https://github.com/tom-xs/nixos-dotfiles --skill define-hypothesis-tom-xs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill eliminates ambiguity in product discovery by forcing teams to articulate clear, falsifiable predictions before committing resources to experiments.

Core Features & Use Cases

  • Structured Framing: Converts vague hunches into the standard We believe that... for... will... as measured by... format.
  • Metric Alignment: Ensures every hypothesis includes primary, secondary, and guardrail metrics to prevent unintended negative impacts.
  • Use Case: Before building a complex recommendation engine, use this skill to define exactly what user behavior change you expect to see and how you will measure it, ensuring the team is aligned on what constitutes success.

Quick Start

Use the define-hypothesis skill to draft a testable prediction for why our trial-to-paid conversion rate is currently declining.

Frequently Asked Questions about define-hypothesis

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

FAQPage Schema
How do I structure assumptions into testable hypotheses for product discovery?▼

To structure assumptions into testable hypotheses, convert vague hunches into a standard format: We believe that... for... will... as measured by... This ensures predictions are falsifiable and metric-driven.

What metrics should I include when defining a hypothesis for experimentation?▼

When defining a hypothesis for experimentation, you should include primary, secondary, and guardrail metrics to prevent unintended negative impacts and ensure rigorous validation.

How do I align my team on what constitutes success before building a new feature?▼

To align your team on success criteria before building, define a testable prediction specifying the expected user behavior change and the exact metrics that will measure it.

When do I need to use falsifiable statements in lean startup validation?▼

You need falsifiable statements in lean startup validation when committing resources to experiments, as they eliminate ambiguity and force evidence-based decision-making.

What is the best way to validate a declining trial-to-paid conversion rate?▼

The best way to validate a declining trial-to-paid conversion rate is to draft a testable prediction using a structured framing format that defines the expected behavior change and clear success metrics.

Related Skills

Explore More Product & Management Skills→
wtthorntonwtthornton
4

analyst

Gathers requirements, maps stakeholders, identifies risks, and estimates effort from feature descriptions.

Community
Advanced
kasperjungekasperjunge
13

opportunity-solution-tree

Map customer problems into an Opportunity Solution Tree for pre-PMF startups.

Community
Basic
math-incmath-inc
1.2k

weights-and-biases

Track ML experiments with automatic logging, visualization, and hyperparameter sweeps.

Official
Intermediate
imMamdouhaboammarimMamdouhaboammar

agency-rapid-prototyper

Scaffold full-stack prototypes with Next.js, Prisma, and Supabase.

Community
Advanced