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Retention Optimization

Checked
eronred·vlatest·marketing·MIT

Predicts customer churn risk & suggests personalized interventions to proactively improve retention rates.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

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

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

eronred

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned eronred/aso-skills at 8de1ee18d8.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
8de1ee18d8
Manifest
skills/retention-optimization/SKILL.md
Scanned
1 file, 164 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill helps AI agents diagnose and improve mobile app retention rates. It acts as an expert in mobile app engagement strategy, identifying potential issues and suggesting a prioritized plan to keep users actively engaged with your application. The skill uses industry benchmarks and a structured framework (Activation, Habit Formation, Engagement Deepening, Long-term Retention) to provide actionable recommendations for improving user retention.

When to use it

  • You're experiencing low Day 1, Day 7, or Day 30 retention rates in your mobile app.
  • You want to understand how your app’s performance compares to industry averages based on category.
  • You need help identifying the root causes of user churn and developing strategies to address them.
  • You're looking for guidance on optimizing onboarding, push notifications, or other engagement features.
  • You are trying to determine if changes (like updates) have impacted retention.

Key capabilities

  • Retention Assessment: Analyzes current retention metrics (Day 1, Day 7, Day 30).
  • Industry Benchmarking: Compares your app's performance against industry averages based on category (Games, Social, Health & Fitness, Productivity, E-commerce, Finance, Education).
  • Framework-Based Diagnosis: Uses a four-stage retention framework to pinpoint problem areas.
  • Personalized Recommendations: Suggests specific optimizations for each stage of the user lifecycle, including onboarding improvements and push notification strategies.

Example prompts

  • "What are typical Day 7 retention rates for a social media app?"
  • "My game has a Day 30 retention rate of 2%. What could be causing this?"
  • "How can I improve the activation phase for my productivity app?"
  • “What should my first push notification say to new users?”

Tips & gotchas

  • The skill requires current retention metrics (Day 1, Day 7, Day 30) and your app's category to provide accurate benchmarks.
  • It also benefits from knowing the monetization model of your application (free vs. subscription).
  • The skill focuses on mobile app retention; its recommendations may not be directly applicable to other types of applications or services.