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Improve Retention

OrganizationPopular
wondelai
improve-retention

Diagnose and fix retention problems using behavior design (B=MAP). Use when the user mentions "users sign up but dont stick around", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users quit after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, see drive-motivation.

Overview

Publisherwondelai
Repositoryskills
Skill nameimprove-retention
Stars
2.2K
Forks
228
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wondelai on GitHub. Read the source before you install it.

Installation

Install the Improve Retention AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wondelai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/improve-retention .claude/skills/improve-retention
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Improve Retention in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Improve Retention on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Improve Retention is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Behavior Design Framework

Framework for designing products that reliably change behavior. Behavior is not about willpower or motivation — it is a design problem with a predictable equation.

Core Principle

The Fogg Behavior Model = B=MAP. Behavior happens when Motivation, Ability, and a Prompt converge at the same moment.

            HIGH ┃
                 ┃   ★ Behavior happens
                 ┃  (above the Action Line)
  Motivation     ┃━━━━━━━━━━━━━━━━━━━━━━━ ← Action Line
                 ┃   ✗ Behavior fails
                 ┃  (below the Action Line)
            LOW  ┃
                 ┗━━━━━━━━━━━━━━━━━━━━━━━━━
                 HARD                    EASY
                        Ability

The Action Line: When motivation and ability are sufficient, a prompt causes the behavior; below the line, no prompt works. High motivation compensates for low ability and vice versa. The reliable strategy is making behaviors easier (move right), not pumping up motivation (move up).

See: references/behavior-model.md when you need the curve mechanics behind this model — the full Action Line math, behavior types (dot/span/path), and a step-by-step failure diagnostic for a behavior that isn't happening.

Scoring

Goal: 10/10. The six Quick Diagnostic rows are the single source of score-movers. Rate each pass/fail, then start at 10 and subtract per failing row: low motivation or below the Action Line (rows 1-2) cost -2 each; prompts, celebration, bottleneck, and scaling (rows 3-6) cost -1.5 each. A design that passes all six scores 10; one that fails every row scores 0. Map to bands: 9-10 = behavior reliably crosses the Action Line at low motivation, prompts are event/anchor-tied, key actions are celebrated; 5-6 = depends on a motivation spike or optimizes a non-bottleneck factor; <=3 = core action below the Action Line, prompts are spam, no habit wiring. Always state the score and name the specific failing rows.

The Three Elements

1. Motivation

Core concept: Motivation is the energy for action, driven by three core motivators, each with two sides: Sensation (pleasure/pain), Anticipation (hope/fear), Belonging (acceptance/rejection). It is powerful but unreliable.

Why it works: Motivation comes in waves — it spikes (New Year's resolutions, product launches) and crashes (day 3, week 2). Products that depend on high motivation fail when the wave recedes; the best designs work at the trough.

Key insights:

  • "Motivation is unreliable. Ability is not." — BJ Fogg
  • Design for low-motivation moments, not peak excitement
  • Motivation-first tactics (inspiring videos, aspirational messaging) produce spikes, not sustained behavior
  • Match required motivation to behavior difficulty — hard behaviors need high motivation

Product applications:

ContextApplicationExample
OnboardingDon't count on the new-user spike lastingFirst actions work even when excitement fades
Re-engagementAssume returning users have low motivationShow immediate value before asking for effort
MessagingTap the right motivatorSocial fitness → belonging; financial tool → hope

Copy patterns:

  • "Takes 30 seconds" (signals ease, lowers motivation needed)
  • "Join 50,000 teams who..." (belonging motivator)
  • "Don't lose your 7-day streak" (anticipation/fear motivator)

Ethical boundary: A fear motivator (the streak pattern above) is fair only when the loss is real and user-owned (their data, their progress); never invent a loss that exists solely to drive a session.

See: references/motivation-waves.md for the three motivators, motivation waves, and designing for troughs.

2. Ability

Core concept: Ability is the capacity to do the behavior — a function of the scarcest resource across six factors (the Ability Chain). If any single link is too weak, the behavior breaks.

Why it works: Unlike motivation, ability can be systematically engineered: every removed field, eliminated step, and preset default moves the behavior right on the model, crossing the Action Line even at low motivation. The Ability Chain gives you the diagnostic — find the weakest link and fix it.

Key insights:

  • Six factors: Time, Money, Physical Effort, Mental Effort, Social Deviance, Non-Routine
  • Simplicity is a function of the scarcest resource — find the bottleneck, not the most obvious factor
  • "Simplicity changes behavior" — BJ Fogg
  • Starter Steps: shrink the behavior to the tiniest version (2 minutes → 30 seconds → one field)
  • Defaults are the most powerful ability tool — users rarely change them

Product applications:

ContextApplicationExample
SignupCut cost across all six factorsOne-click SSO removes time, mental effort, non-routine
Core actionFix the weakest linkMental-effort bottleneck → smart defaults and templates
Enterprise adoptionAddress social deviance"Your team already uses this" reduces social risk

Copy patterns:

  • "One click to get started" (time + physical effort)
  • "No technical skills needed" (mental effort)
  • "Works just like tools you already use" (non-routine)

Ethical boundary: Reduce friction only on genuinely valuable behaviors — never make it too easy to overspend, over-share, or delete important data without confirmation.

See: references/ability-chain.md for the six factors in detail, friction audit templates, and simplification strategies.

3. Prompt

Core concept: The prompt says "do it now." Without one, behavior doesn't happen regardless of motivation and ability. Three types: Person Prompts (internal reminders), Context Prompts (environmental cues), Action Prompts (designed triggers from the product).

Why it works: Teams assume motivation + ability is enough — it isn't, not without a well-timed prompt. But prompts only work above the Action Line: a push notification to someone lacking motivation or ability is spam.

Key insights:

  • A prompt at the wrong moment is noise; at the right moment, magic
  • Anchor moments tie new behaviors to existing routines ("After I open Slack, I will...")
  • Prompt fatigue is real — every unnecessary prompt degrades the value of future ones

Product applications:

ContextApplicationExample
NotificationsPrompt only above the Action LineSend digest when there's content to review, not on a schedule
Re-engagementTie prompts to real events"Your report is ready" (event-based, not time-based)
Feature discoveryPrompt when motivation and ability alignFeature tour appears when user hits the problem it solves

Copy patterns:

  • "Your weekly report is ready" (context prompt — real event)
  • "One thing left to complete your setup" (action prompt with progress)
  • Never: "We miss you!" (product need, not user need)

Ethical boundary: Every prompt must pass the test "Would I appreciate receiving this right now?" — if it serves a product metric (DAU, re-engagement) but not the user's current goal, cut it.

See: references/prompt-design.md for prompt types, timing strategies, notification design, and anchor moments.

Tiny Habits Method

The practical application of B=MAP: make behaviors so small they need almost no motivation, anchor them to existing routines, and celebrate immediately.

The Recipe

After I [ANCHOR MOMENT], I will [TINY BEHAVIOR], then I [CELEBRATION].
  • Anchor Moment: an existing routine that reliably happens (opening an app, finishing a meeting, morning coffee).
  • Tiny Behavior: the smallest version of the target behavior — not "write a report" but "open the report template."
  • Celebration: an immediate positive emotion that wires the habit. Repetition alone isn't enough — you need the feeling of success.

Starter Steps

Every target behavior has a Starter Step — the tiniest meaningful version:

Target BehaviorStarter StepWhy It Works
Complete onboardingFill in one fieldMomentum from completion
Use analytics dailyOpen the dashboardSeeing data creates curiosity
Collaborate with teamSend one commentSocial reciprocity kicks in

Scaling Behaviors

Once wired, tiny behaviors grow naturally: open dashboard → check a few metrics → customize → automatic morning habit. Never force scaling — let motivation and momentum drive expansion. The tiny version is the foundation, not a failure.

See: references/tiny-habits.md for the full Tiny Habits recipe, celebration techniques, and scaling patterns.

Behavior Design Process

Fogg's systematic process for lasting behavior change:

Step 1: Clarify the Aspiration

What outcome does the user want — their aspiration, not the product's goal ("stay on top of my team's progress", not "increase DAU").

Step 2: Explore Behavior Options

List all possible behaviors that could achieve the aspiration. Be exhaustive — don't commit yet.

Step 3: Match Behaviors

Assess each for motivation and ease; plot on a 2×2 of impact vs. feasibility (Focus Mapping).

Step 4: Start Tiny

Shrink the best-matched behavior to its Starter Step; design the prompt; add celebration.

Step 5: Optimize

Expand once wired. Fix bottlenecks with the Ability Chain; refine prompt timing from data.

See: references/product-applications.md when applying this process to a specific category — B=MAP mapped to SaaS onboarding, mobile, e-commerce, health, and education with per-category motivation timelines and bottlenecks.

The Action Line

Moving Behaviors Above the Action Line

  • Increase Ability (move right) — remove steps, pre-fill, defaults, templates, wizards. The most reliable approach.
  • Find better Prompts — anchor to existing routines; event-based beats time-based; trigger when motivation is naturally higher.
  • Increasing Motivation (move up) is unreliable — if you need motivation tactics, the behavior is probably too hard.

Retention Diagnostics with B=MAP

Map B=MAP to product metrics:

MetricB=MAP DiagnosisAction
Low activationFirst action below the Action LineShrink onboarding to Starter Step; fix weakest Ability Chain link
Day-1 drop-offPrompt failed or mistimedRedesign first-day prompts; anchor to an existing routine
Day-7 drop-offMotivation wave receded, behavior too hardReduce core action difficulty
Day-30 drop-offHabit didn't form, no internal promptCreate tiny habit recipe; add celebration loops
Low feature adoptionFeature below the Action Line for most usersFriction-audit it; prompt only when motivation is present
Notification fatiguePrompts sent below the Action LineCut volume; send only with motivation + ability

See: references/case-studies.md for a worked diagnosis of Instagram, Duolingo, Slack, Calm, and Peloton — read it to see how M, A, and P are scored independently on a real product before diagnosing your own.

Common Mistakes

MistakeWhy It FailsFix
Relying on motivation for retentionMotivation always recedes; products needing it fail at the troughMake behaviors tiny enough to survive motivation dips
Ignoring the Ability Chain bottleneckYou optimized time but the barrier is mental effort or social devianceAudit all six factors; fix the scarcest resource
Prompting below the Action LineNotifications to unmotivated/unable users = spamEvent-based triggers only when motivation + ability suffice
Skipping celebration in onboardingWithout positive emotion, repetition doesn't wire habitsAdd success states and micro-celebrations after key actions
First action too ambitious"Complete your profile" is a project, not a behaviorShrink to Starter Step: one field, one action
Copying products without diagnosing B=MAPA high-motivation audience's design fails yoursDiagnose your users' motivation, ability, and prompt context first

Quick Diagnostic

QuestionIf NoAction
Can a new user do the core action in under 60 seconds?Ability too lowFriction audit; shrink to Starter Step
Does the product work when motivation is low?Design depends on spikesRedesign core behaviors for minimal motivation
Are prompts tied to real events or anchors?Prompts feel like spamSwitch to event-based or anchor-based prompts
Is there immediate feedback after key actions?No celebration = no habit wiringAdd success states, progress, social feedback
Have you found the weakest Ability Chain link?Optimizing the wrong thingRate each of the six factors 1-5 for the core behavior
Do users scale naturally from tiny behaviors?Forcing complexity too earlyStarter Steps; let behaviors grow organically

Further Reading

Based on BJ Fogg's behavior design research:

About the Author

BJ Fogg, PhD founded the Behavior Design Lab at Stanford University, where he has researched behavior change since 1998. He created the Fogg Behavior Model (B=MAP), coined the term "behavior design", and trained thousands of innovators — including Instagram co-founder Mike Krieger. Tiny Habits distills two decades of that research: lasting change comes from behaviors that are tiny, anchored, and celebrated.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Improve Retention AI skill do?

Diagnose and fix retention problems using behavior design (B=MAP). Use when the user mentions "users sign up but dont stick around", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users quit after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, se...

Why use Improve Retention on TypingMind?

Because you install it once and use it with any model. Improve Retention is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Improve Retention in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wondelai/skills/tree/main/improve-retention. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Improve Retention?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Improve Retention?

As many as you like. As long as a model supports skills, you can use Improve Retention with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Improve Retention AI skill free?

Yes. It is published on GitHub by wondelai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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