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danielpradilla
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retention-diagnostic

Segment user cohorts, identify retention inflection points, and recommend interventions.

Updated May 18, 2026
Search Tags:#monitoring#retention#intervention#product health#user analysis
One-click install
npx skills add https://github.com/danielpradilla/project-product-skills --skill retention-diagnostic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: retention-diagnostic
Source: https://github.com/danielpradilla/project-product-skills/tree/main/skills/retention-diagnostic
Command: npx skills add https://github.com/danielpradilla/project-product-skills --skill retention-diagnostic

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a framework to diagnose why users leave, identify what keeps them, and recommend specific interventions to improve retention.

Core Features & Use Cases

  • Retention Analysis: Analyze retention metrics, such as D1, D7, D30, DAU/MAU, and churn rate.
  • Investigation Framework: Segment users, identify inflection points, correlate early behaviors, and qualify churn.
  • Intervention Recommendations: Recommend specific, testable interventions based on the analysis.
  • Monitoring Plan: Develop a monitoring plan with metrics and alert thresholds.

Quick Start

Use the retention-diagnostic skill to analyze user retention patterns and identify interventions for your product.

Frequently Asked Questions about retention-diagnostic

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

FAQPage Schema
How do I diagnose user retention issues and identify why users leave?▼

To diagnose user retention issues, you need to segment user cohorts, identify inflection points in user behaviors, and correlate early actions with churn. This framework qualifies churn reasons by analyzing retention metrics alongside user interviews.

What is the best way to analyze retention metrics like D1, D7, and D30?▼

Analyzing retention metrics like D1, D7, D30, and DAU/MAU involves segmenting users into cohorts and identifying behavioral inflection points. This process correlates early user behaviors with long-term retention patterns to pinpoint drop-off reasons.

How can I recommend specific interventions to improve product health and reduce churn?▼

You can recommend specific interventions to reduce churn by correlating early user behaviors with retention drop-off points. This approach generates testable recommendations targeted at the exact inflection points where user cohorts disengage.

Do I need user interviews to investigate retention patterns and user analysis?▼

Yes, understanding user behaviors through user interviews is required alongside quantitative retention metrics. Combining cohort analysis with qualitative interview data ensures accurate diagnosis of why users leave or stay.

How do I develop a monitoring plan for product health and retention metrics?▼

Developing a monitoring plan for product health requires defining specific retention metrics and establishing alert thresholds. This plan tracks the impact of your interventions on user cohorts over time to ensure sustained retention.

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