improve-retention

Diagnose and fix user retention problems using the Fogg Behavior Model framework.

Updated Jun 27, 2026
One-click install
npx skills add https://github.com/rachmadideni/ai-staff-assistant --skill improve-retention-rachmadideni
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Skill: improve-retention
Source: https://github.com/rachmadideni/ai-staff-assistant/tree/main/.agents/skills/improve-retention
Command: npx skills add https://github.com/rachmadideni/ai-staff-assistant --skill improve-retention-rachmadideni

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Users sign up but don't stick around — activation rates stall, cohorts drop off after day one, and churn analysis reveals no clear cause. This Skill diagnoses retention failures through the Fogg Behavior Model (B=MAP) and prescribes concrete fixes across motivation, ability, and prompt design. ## Core Features & Use Cases - B=MAP Retention Diagnostics: Map metrics like day-1 drop-off, low activation, and notification fatigue to specific motivation, ability, or prompt failures with targeted fixes. - Ability Chain Friction Audits: Rate six simplicity factors (time, money, physical effort, mental effort, social deviance, non-routine) to find the weakest link blocking key behaviors. - Tiny Habits & Prompt Design: Design starter steps, anchor moments, celebration loops, and event-based notifications that survive motivation troughs. - Use Case: When a SaaS product shows strong signups but 70% day-7 churn, use this Skill to run a friction audit on onboarding, shrink the first action to a starter step, and redesign prompts to be event-based rather than schedule-based. ## Quick Start Analyze why users drop off after their first session in my onboarding flow and recommend fixes using the B=MAP framework.

Frequently Asked Questions about improve-retention

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

FAQPage Schema
How do I diagnose why users drop off after signing up?▼

Map the failing behavior to B=MAP: check whether a prompt exists, then audit ability across six factors (time, money, physical effort, mental effort, social deviance, non-routine), and only then examine motivation. Day-1 drop-off usually indicates a prompt failure, while day-7 drop-off signals the behavior is too hard for receding motivation.

What is the Fogg Behavior Model B=MAP?▼

B=MAP states that behavior occurs when Motivation, Ability, and a Prompt converge at the same moment. Behaviors above the Action Line succeed when prompted; below it, no prompt works. The reliable design strategy is increasing ability (making behaviors easier) rather than boosting motivation.

How do I run a friction audit on my onboarding flow?▼

Rate each of the six Ability Chain factors from 1-5 for the target behavior: time, money, physical effort, mental effort, social deviance, and non-routine. The lowest-rated factor is the bottleneck blocking the behavior. Fix that weakest link first, typically with smart defaults, templates, or progressive disclosure.

When should I use behavior design versus the Hook Model for retention?▼

Use B=MAP for diagnosis when a specific behavior is failing and you need to identify whether motivation, ability, or prompts are the cause. Use the Hook Model for designing self-reinforcing engagement loops with triggers, variable rewards, and investment. The two frameworks complement each other.

Why do push notifications fail to re-engage users?▼

Notifications fail when sent to users below the Action Line — lacking motivation or ability, the prompt becomes spam. Schedule-based messages like "We miss you!" underperform event-based prompts tied to real activity, such as "Your report is ready." Prompt fatigue also degrades the value of all future notifications.

What are the limitations of motivation-first retention strategies?▼

Motivation comes in waves that spike at signup and crash by day 14-30, so products depending on it fail at the trough. Motivation tactics don't compound and are context-dependent. Ability-first design — shrinking behaviors to starter steps that survive low motivation — produces more durable retention.