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gvkhosla
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churn-diagnostician

Diagnose churn root causes across time-to-churn, behavioral gaps, and feedback signals.

8|Updated Feb 18, 2026
Search Tags:#onboarding#analytics#retention#experimentation#product-management#churn
One-click install
npx skills add https://github.com/gvkhosla/founder-skills --skill churn-diagnostician
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: churn-diagnostician
Source: https://github.com/gvkhosla/founder-skills/tree/main/skills/pmf/churn-diagnostician
Command: npx skills add https://github.com/gvkhosla/founder-skills --skill churn-diagnostician

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Churn is the product's clearest feedback signal when analyzed systematically. The Churn Diagnostician provides a structured, evidence-based approach to identify the root cause of churn and translate it into an actionable experiment to test in one cycle.

Core Features & Use Cases

  • Parallel, multi-angle churn investigations (time-to-churn, behavioral gaps, and feedback signals) to surface root causes.
  • Automated synthesis into a concise churn diagnosis with root cause, confidence level, and a concrete experiment plan.
  • Suitable for product teams pursuing PMF improvements, onboarding optimization, and retention loop design.

Quick Start

Explain why users churn and generate a churn-diagnosis with a root cause and an actionable experiment.

Frequently Asked Questions about churn-diagnostician

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

FAQPage Schema
How do I diagnose the root cause of user churn in my product?▼

To diagnose user churn, analyze time-to-churn, behavioral gaps, and feedback signals. This converges on a root cause and produces a prioritized churn-diagnosis with a recommended experiment.

How do I plan a quick-win experiment to improve user retention?▼

Planning a retention experiment requires a churn-diagnosis output that provides a concrete, testable 4-6 week experiment timeline and defined success metrics based on identified behavioral gaps.

What is the best way to investigate onboarding drop-off and behavioral gaps?▼

Investigating onboarding behavioral gaps involves analyzing user feedback signals and time-to-churn data to surface root causes, systematically translating product feedback into actionable retention experiments.

Can I use churn analysis to generate testable outcomes for product management?▼

Yes, churn analysis generates testable product management outcomes by synthesizing multi-angle investigations into a concise diagnosis with a confidence level and a targeted 4-6 week experiment plan.

Does this churn diagnosis approach work for optimizing product-market fit?▼

Yes, this approach suits product teams pursuing product-market fit improvements, onboarding optimization, and retention loop design by using churn as a clear feedback signal for systematic analysis.

Why does my user retention drop after onboarding and how do I fix it?▼

Retention drops after onboarding due to behavioral gaps identified through time-to-churn analysis. Fix it by running a prioritized 4-6 week experiment targeting the root cause with specific success metrics.

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