AGENT SKILLBY ERONRED
Retention optimization
AT A GLANCE
Retention optimization is a growth skill for AI agents. Provides expert guidance for guidance for mobile app retention and engagement strategy. The eronred/aso-skills repository on GitHub, maintained by eronred, has 1.6K stars and the MIT license. To add it, run npx skills add eronred/aso-skills --skill retention-optimization
- SOURCE REPOSITORY
- eronred/aso-skills (opens GitHub in a new tab)
- REPO STARS
- SOURCE REPOSITORY
- LICENSE
- MIT
- CATEGORIES
- SKILL.MD SIZE
- 5.3 KB159 LINES
QUICK ANSWER
- WHAT IT IS01
- Provides expert guidance for guidance for mobile app retention and engagement strategy. What sets it apart is how it narrows retention optimization into one specific workflow rather than a broad, generic prompt.
- INPUTS02
- Context the agent needs: your goals, audience, constraints, and any source material the skill asks for.
- OUTPUT03
- A ready-to-use result: the analysis, copy, or recommendations the agent produces.
INSTALL
One command adds this skill from its source repository.
npx skills add eronred/aso-skills --skill retention-optimizationREAD THE SKILL.MD BELOW BEFORE YOU RUN IT. SKILLS INSTRUCT YOUR AGENT.
SKILL.MD
The full file, as published in the source repository. Copy it, download it or open it on GitHub.
ON THIS PAGE · 16
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#
- Check for
app-marketing-context.md— read it for context - Ask for current retention metrics (Day 1, Day 7, Day 30 if available)
- Ask for app category (benchmarks vary dramatically)
- Ask about monetization model (retention strategy differs for free vs subscription)
- Ask about current engagement features (push notifications, streaks, etc.)
Retention Benchmarks#
Industry Averages (Day 1 / Day 7 / Day 30)#
| Category | Day 1 | Day 7 | Day 30 | Good |
|---|---|---|---|---|
| Games | 25-30% | 10-15% | 3-5% | D1 >35%, D30 >8% |
| Social | 30-35% | 15-20% | 8-12% | D1 >40%, D30 >15% |
| Health & Fitness | 20-25% | 10-12% | 4-6% | D1 >30%, D30 >10% |
| Productivity | 15-20% | 8-10% | 3-5% | D1 >25%, D30 >8% |
| E-commerce | 15-20% | 5-8% | 2-3% | D1 >25%, D30 >5% |
| Finance | 20-25% | 10-12% | 5-8% | D1 >30%, D30 >10% |
| Education | 15-20% | 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#
Push Notification Strategy#
| Timing | Message Type | Example |
|---|---|---|
| Day 1 | Welcome + quick tip | "Tap here to set up your first [X]" |
| Day 3 | Value reminder | "Your [data/content] is ready to view" |
| Day 5 | Social proof | "[N] people completed [action] this week" |
| Day 7 | Streak/progress | "You're building a great habit!" |
| Day 14 | Feature discovery | "Did you know you can also [feature]?" |
| Day 30 | Milestone | "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:
- Email (if you have it) — "We've added [feature] since you last visited"
- Push notification — "[Specific value] is waiting for you"
- In-app message (on return) — "Welcome back! Here's what's new"
Cancellation Flow (Subscriptions)#
When a user tries to cancel:
- Ask why (multiple choice)
- 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
- Offer pause instead of cancel
- 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 usersAction Plan#
Week 1 (Quick Wins):
- [specific tactic with expected impact]
- [specific tactic with expected impact]
Month 1 (High Impact):
- [specific tactic with expected impact]
- [specific tactic with expected impact]
Quarter 1 (Strategic):
- [specific tactic with expected impact]
- [specific tactic with expected impact]
Related Skills#
app-analytics— Set up retention trackingmonetization-strategy— Retention's impact on revenuereview-management— Retention issues surface in reviewsapp-launch— First-time user experience
FAQ
07 QUESTIONS
01What is the Retention optimization agent skill?
Provides expert guidance for guidance for mobile app retention and engagement strategy. It is listed under growth in the 100xLab agent skills directory.
02How do I install Retention optimization in Cursor, Claude Code, or Codex?
Run npx skills add eronred/aso-skills --skill retention-optimization in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only Retention optimization, not every skill in the repository.
03What does the Retention optimization SKILL.md cover?
The SKILL.md includes sections on Initial Assessment, Retention Benchmarks, Industry Averages (Day 1 / Day 7 / Day 30), Retention Framework, Activation (Day 0-1) and Habit Formation (Day 1-7). Read the full text on this page or in plain markdown at /resources/agent-skills/eronred-aso-skills--retention-optimization--bo0cmj/raw.
04Which agents can use Retention optimization?
Retention optimization installs with the cross-agent skills installer (npx skills add), which sets it up for agents such as Claude Code, Cursor and Codex. Each agent loads the SKILL.md when you ask for the skill by name.
05What license is Retention optimization under?
The eronred/aso-skills repository declares the MIT license. Read the license file in the repository before you copy or redistribute the skill.
06Is Retention optimization free to use?
Reading, copying and installing Retention optimization costs nothing: it is published in a public GitHub repository under the MIT license. Reuse and redistribution follow that license.
07Where is the source of Retention optimization?
Retention optimization comes from https://github.com/eronred/aso-skills, which has 1.6K GitHub stars. The SKILL.md is at https://github.com/eronred/aso-skills/blob/HEAD/skills/retention-optimization/SKILL.md. The author is eronred.
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ATTRIBUTION
Retention optimization was written by its original authors and is published in eronred/aso-skills (opens GitHub in a new tab) under the MIT license. All rights stay with the original authors.
100xLab did not write, maintain or endorse this skill. This page lists it for discovery. Repo stars belong to the source repository, not to the skill. Review any skill before you install it.