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Agent skill

Retention Optimization

by appeeky in appeeky/aso-skills

When the user wants to reduce churn, improve user engagement, or increase lifetime value.

MITAuto-check passedMarketing & SEO

Install Retention Optimization

skills CLI
$ npx skills add appeeky/aso-skills --skill retention-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install appeeky/aso-skills retention-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/appeeky/aso-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/retention-optimization .claude/skills/retention-optimization && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
retention-optimization
GitHub stars
2.2k
Token cost
~1.5k tokens
SKILL.md length
677 words
Files
1
Skills in repo
39
Repo updated
First seen
Licence
MIT

At a glance

When the user wants to reduce churn, improve user engagement, or increase lifetime value.

  • Works in 4 steps: Activation (Day 0-1) → Habit Formation (Day 1-7) → Engagement Deepening (Day 7-30) → …
  • Wants to reduce churn
  • SKILL.md covers Initial Assessment, Retention Benchmarks, Retention Framework and Churn Prevention Tactics, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Retention Optimization is an agent skill from appeeky/aso-skills. When the user wants to reduce churn, improve user engagement, or increase lifetime value. Also use when the user mentions "retention", "churn", "users leaving", "engagement", "DAU/MAU", "user activation", or "why are users uninstalling". For onboarding-specific issues, see app-launch. For monetization, see monetization-strategy.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Referral and retention marketing. The repository describes itself as: AI agent skills for App Store Optimization (ASO) and app marketing. Built for indie developers, app marketers, and growth teams who want Cursor, Claude Code, or any Agent… The licence is MIT.

When your agent uses it

  • Wants to reduce churn
  • Improve user engagement
  • Increase lifetime value
  • The user mentions retention

Example prompts

  • “retention”
  • “users leaving”
  • “engagement”
  • “/retention-optimization”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Activation (Day 0-1)
  2. Habit Formation (Day 1-7)
  3. Engagement Deepening (Day 7-30)
  4. Long-term Retention (Day 30+)

What it can do on your machine

Read from SKILL.md and the folder at commit 3919d7c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Retention Optimization loads about 1.5k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 677 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from appeeky/aso-skills at commit 3919d7c, republished under its MIT licence (© appeeky). 677 words, ~1,455 tokens.

Download SKILL.mdSave it as .claude/skills/retention-optimization/SKILL.md (or your agent's skills folder).
name
retention-optimization
description
When the user wants to reduce churn, improve user engagement, or increase lifetime value. Also use when the user mentions "retention", "churn", "users leaving", "engagement", "DAU/MAU", "user activation", or "why are users uninstalling". For onboarding-specific issues, see app-launch. For monetization, see monetization-strategy.
metadata.version
1.0.0

Retention Optimization

You are an expert in mobile app retention and engagement strategy. Your goal is to diagnose retention issues and provide a prioritized plan to keep users coming back.

Initial Assessment

  1. Check for app-marketing-context.md — read it for context
  2. Ask for current retention metrics (Day 1, Day 7, Day 30 if available)
  3. Ask for app category (benchmarks vary dramatically)
  4. Ask about monetization model (retention strategy differs for free vs subscription)
  5. Ask about current engagement features (push notifications, streaks, etc.)

Retention Benchmarks

Industry Averages (Day 1 / Day 7 / Day 30)
CategoryDay 1Day 7Day 30Good
Games25-30%10-15%3-5%D1 >35%, D30 >8%
Social30-35%15-20%8-12%D1 >40%, D30 >15%
Health & Fitness20-25%10-12%4-6%D1 >30%, D30 >10%
Productivity15-20%8-10%3-5%D1 >25%, D30 >8%
E-commerce15-20%5-8%2-3%D1 >25%, D30 >5%
Finance20-25%10-12%5-8%D1 >30%, D30 >10%
Education15-20%8-10%3-5%D1 >25%, D30 >8%

Retention Framework

1. Activation (Day 0-1)

The first session determines everything. Users who don't reach the "aha moment" in session 1 rarely return.

Diagnose:

  • What % of users complete onboarding?
  • How long until the first value moment?
  • What's the drop-off point in the first session?

Optimize:

  • Reduce time-to-value (show core value in < 60 seconds)
  • Remove unnecessary onboarding steps
  • Defer account creation until after value delivery
  • Use progressive disclosure (don't overwhelm)
  • Show a "quick win" in the first session
2. Habit Formation (Day 1-7)

Diagnose:

  • What triggers bring users back?
  • Is there a natural usage frequency?
  • What do retained users do that churned users don't?

Optimize:

  • Push notifications — Personalized, value-driven, not spammy
    • Day 1: "Welcome back — here's what you missed"
    • Day 3: "[Specific value] is waiting for you"
    • Day 7: "You're on a [N]-day streak!"
  • Streaks & progress — Visual progress indicators
  • Daily content — New content, challenges, or recommendations
  • Social hooks — Friends, leaderboards, sharing
3. Engagement Deepening (Day 7-30)

Diagnose:

  • Which features do power users use that casual users don't?
  • What's the engagement cliff (when do users stop exploring)?

Optimize:

  • Feature discovery prompts (introduce advanced features gradually)
  • Personalization (adapt content/recommendations to usage patterns)
  • Community features (forums, social, user-generated content)
  • Achievement system (badges, milestones, rewards)
4. Long-term Retention (Day 30+)

Diagnose:

  • What causes late-stage churn?
  • Are there seasonal patterns?
  • Do updates improve or hurt retention?

Optimize:

  • Regular content updates
  • Feature launches that re-engage dormant users
  • Win-back campaigns for churned users
  • Loyalty rewards for long-term users

Churn Prevention Tactics

Show full SKILL.md (275 more words)Show less
Push Notification Strategy
TimingMessage TypeExample
Day 1Welcome + quick tip"Tap here to set up your first [X]"
Day 3Value reminder"Your [data/content] is ready to view"
Day 5Social proof"[N] people completed [action] this week"
Day 7Streak/progress"You're building a great habit!"
Day 14Feature discovery"Did you know you can also [feature]?"
Day 30Milestone"One month! Here's your progress summary"

Rules:

  • Max 3-5 notifications per week
  • Always provide value, never just "Come back!"
  • Personalize based on user behavior
  • Allow granular notification preferences
  • A/B test timing and copy
Win-back Campaigns

For users who haven't opened the app in 7+ days:

  1. Email (if you have it) — "We've added [feature] since you last visited"
  2. Push notification — "[Specific value] is waiting for you"
  3. In-app message (on return) — "Welcome back! Here's what's new"
Cancellation Flow (Subscriptions)

When a user tries to cancel:

  1. Ask why (multiple choice)
  2. Offer alternatives based on reason:
    • "Too expensive" → Offer discount or downgrade
    • "Don't use enough" → Show usage stats, suggest features
    • "Missing feature" → Share roadmap, offer to notify
    • "Found alternative" → Highlight unique value
  3. Offer pause instead of cancel
  4. Make it easy to cancel (forced retention backfires)

Output Format

Retention Diagnostic
Current State:
- Day 1: [X]% (benchmark: [Y]%) [above/below]
- Day 7: [X]% (benchmark: [Y]%) [above/below]
- Day 30: [X]% (benchmark: [Y]%) [above/below]

Biggest Drop-off: Day [N] to Day [N]
Estimated Impact: [X]% improvement = [Y] additional monthly users
Action Plan

Week 1 (Quick Wins):

  1. [specific tactic with expected impact]
  2. [specific tactic with expected impact]

Month 1 (High Impact):

  1. [specific tactic with expected impact]
  2. [specific tactic with expected impact]

Quarter 1 (Strategic):

  1. [specific tactic with expected impact]
  2. [specific tactic with expected impact]
  • app-analytics — Set up retention tracking
  • monetization-strategy — Retention's impact on revenue
  • review-management — Retention issues surface in reviews
  • app-launch — First-time user experience

© appeeky, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/retention-optimization of appeeky/aso-skills.

Open the folder on GitHubat commit 3919d7c

Compare with similar skills

Retention Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Retention Optimization compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Retention Optimization this skillappeeky/aso-skills2.2k—~1.5kAutomated safety check: PassMIT
Lapsed Usergustavscirulis/snapgrid1171 repos~2.5kAutomated safety check: NotesCustom licence
Subscription Lifecyclegustavscirulis/snapgrid1171 repos~3kAutomated safety check: NotesCustom licence
Referralscoreyhaines31/marketingskills54k2 repos~2.4kAutomated safety check: PassMIT
Referral Programfreekmurze/dotfiles1k17 repos~1.8kAutomated safety check: PassNone
Churn Preventionrongxinzy/RongxinAI1543 repos~2.6kAutomated safety check: PassMIT

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Categories

Questions about Retention Optimization

What does Retention Optimization do?

When the user wants to reduce churn, improve user engagement, or increase lifetime value. Retention Optimization is an agent skill from appeeky/aso-skills. When the user wants to reduce churn, improve user engagement, or increase lifetime value.

When should I use Retention Optimization?

Retention Optimization fits situations like: wants to reduce churn; improve user engagement; increase lifetime value; the user mentions retention.

How do I install Retention Optimization in Claude Code?

Run `npx skills add appeeky/aso-skills --skill retention-optimization -a claude-code`. Or copy the skill folder (skills/retention-optimization in appeeky/aso-skills) into .claude/skills/retention-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Retention Optimization in Codex?

Run `npx skills add appeeky/aso-skills --skill retention-optimization -a codex`. Or copy the skill folder (skills/retention-optimization in appeeky/aso-skills) into .agents/skills/retention-optimization in your project. Codex loads it when a task matches its description.

Can I use Retention Optimization in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add appeeky/aso-skills --skill retention-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/retention-optimization, .gemini/skills/retention-optimization, .github/skills/retention-optimization and .opencode/skills/retention-optimization in your project.

What does Retention Optimization need to run?

SKILL.md names no scripts, command-line tools or credentials: Retention Optimization is instructions for the agent only.

Does Retention Optimization access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Retention Optimization safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Retention Optimization use?

Retention Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Retention Optimization use?

About 1.5k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Retention Optimization?

Skills that share tags, products or a category with Retention Optimization: Lapsed User (gustavscirulis/snapgrid, 117 stars), Subscription Lifecycle (gustavscirulis/snapgrid, 117 stars), Referrals (coreyhaines31/marketingskills, 54k stars) and Referral Program (freekmurze/dotfiles, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Retention Optimization?

appeeky (a GitHub organization) maintains it in appeeky/aso-skills, which has 2,152 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on October 6, 2026.

Source: appeeky/aso-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.