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growth-diagnosis

Diagnose growth bottlenecks by analyzing acquisition channels, conversion funnels, and retention patterns.

8|2|Updated May 3, 2026
Search Tags:#growth#retention#experimentation#cohort analysis#funnel analysis#marketing channels#measurement maturity
One-click install
npx skills add https://github.com/ejoongseok/claude-settings --skill growth-diagnosis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: growth-diagnosis
Source: https://github.com/ejoongseok/claude-settings/tree/main/claude-code/skills/growth-diagnosis
Command: npx skills add https://github.com/ejoongseok/claude-settings --skill growth-diagnosis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Growth-diagnosis helps you understand why customers come in, convert, stay, and refer—so you can identify the biggest growth bottleneck and the highest-leverage experiments for the next quarter.

Core Features & Use Cases

  • Acquisition & Channel Diagnosis: Evaluate inbound channels by effectiveness signals like CAC, ROI, and activation conversion to decide what to strengthen or test.
  • Conversion Funnel Analysis: Analyze step-by-step drop-offs across awareness → signup → activation → paid → renewal, and propose measurable hypotheses and experiments.
  • Activation, Retention & Referral Insights: Assess activation and retention patterns (e.g., cohorts, churn timing, core action reach) and connect expansion/viral opportunities to customer behavior and data confidence.
  • Content & Brand Asset Review: Inventory content/SEO/brand assets and highlight the biggest asset gap affecting conversion and discovery.
  • Next-Quarter Growth Hypotheses: Produce 1–2 prioritized growth levers with an explicit measurement promise and guardrails to avoid vanity metrics.

Quick Start

Run growth-diagnosis in the full mode for your current product and include any available analytics signals and customer/prospect history so it can return a comprehensive growth report with prioritized experiments.

Frequently Asked Questions about growth-diagnosis

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

FAQPage Schema
How do I diagnose growth funnel drop-offs and find the highest-leverage bottleneck?▼

Diagnose funnel drop-offs by analyzing step-by-step conversion rates across awareness, signup, activation, paid, and renewal. This identifies the highest-leverage bottlenecks and produces measurable hypotheses and targeted experiments for the next quarter.

What is cohort analysis used for when diagnosing user retention and churn patterns?▼

Cohort analysis assesses retention patterns and churn timing by grouping users by activation periods. This reveals when core action reach drops off and connects expansion or viral referral opportunities to actual customer behavior data.

How do I evaluate acquisition channels and decide what to strengthen or test?▼

Evaluate acquisition channels by analyzing effectiveness signals like CAC, ROI, and activation conversion rates. This diagnosis determines which marketing channels are underperforming and decides what to strengthen or test based on data confidence.

Do I need local project context files to run a full-quarter growth diagnosis?▼

Yes, a full-quarter growth diagnosis requires reading local project context files like CLAUDE.md, bot index, customer reports, and business rules. It assesses analytics measurement maturity before producing data-grounded hypotheses and experiment recommendations.

Can I run focused growth diagnosis modes for just retention or funnel analysis?▼

Yes, focused growth diagnosis modes are available for retention, funnel analysis, acquisition channels, or delta comparison instead of a full-quarter review. Each mode uses existing repo context to identify specific bottlenecks and propose targeted experiments.

What's the best way to prioritize growth experiments and avoid vanity metrics?▼

Prioritize growth experiments by selecting one to two high-leverage growth levers with explicit measurement promises and guardrails. This avoids vanity metrics by ensuring experiment recommendations are tied to data-grounded hypotheses and measurable outcomes.

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