Analyze SQL-based funnel data to identify drop-offs in multi-step user journeys.
npx skills add https://github.com/cncorp/arsenal --skill funnel-analysis
Please help me install this Agent Skill. Skill: funnel-analysis Source: https://github.com/cncorp/arsenal/tree/main/dot-claude/skills/funnel-analysis Command: npx skills add https://github.com/cncorp/arsenal --skill funnel-analysis
This Skill provides SQL-based templates to analyze user conversion funnels and identify drop-off points in both standard and onboarding funnels.
Use the provided SQL templates to compute funnel metrics for your data and interpret the results to optimize onboarding and activation flows.
High-intent search queries and answers about installing and using this skill.
Funnel analysis reveals drop-off points by computing per-step metrics—users, conversion rate, and drop-off percentage—across multi-step user journeys. SQL templates extract and aggregate your data to show exactly where users stall or churn in standard and onboarding funnels.
Standard funnels track conversion over 28+ days for general user flows, while onboarding funnels focus on the first 7+ days of user activation. Both use the same SQL-based approach but apply different time windows to diagnose where users fail to complete initial steps versus long-term retention stages.
The Skill includes ready-to-use SQL templates you adapt to your schema, so basic SQL familiarity helps but you can modify provided queries. Templates handle the aggregation and metric computation; you supply your data structure.
Funnel analysis works with predefined steps baked into the templates for standard and onboarding flows. You extract and adapt the SQL to match your step definitions and data schema, then compute metrics for each step.
Per-step metrics include step name, user count, percentage of starting users, conversion rate to the next step, and drop-off quantity. These outputs let you pinpoint exactly which steps lose the most users and by what margin.
Once funnel analysis identifies where users stall in onboarding, you use drop-off metrics and conversion rates to prioritize fixes—simplifying high-drop steps, removing friction, or adjusting messaging. Re-run analysis after changes to measure improvement.
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