retention-optimization
by EronredThe retention-optimization skill helps Product Management teams diagnose churn, improve engagement, and raise lifetime value with benchmark-aware, prioritized recommendations. Use it when you need a retention-optimization guide for Day 1, Day 7, and Day 30 retention, or when users ask why users leave, stop returning, or uninstall.
This skill scores 78/100, which means it is a solid directory candidate: users can likely trigger it reliably and get useful retention guidance without starting from a blank prompt. The score is not higher because the workflow is mostly self-contained in SKILL.md and lacks support files, examples, or install-time tooling that would make adoption even clearer.
- Strong triggerability: the description names retention, churn, DAU/MAU, activation, and uninstall scenarios, with explicit routing to related skills.
- Operational workflow is concrete: it asks for Day 1/7/30 metrics, app category, monetization model, and current engagement features before advising.
- Good decision support: it includes retention benchmarks by category and a structured retention framework, giving agents more than generic strategy advice.
- No supporting files or scripts: the skill appears to rely entirely on one markdown file, so agents get no extra automation or reference material.
- The excerpt shows a truncated framework section and no visible constraints section, so some execution details may still require interpretation.
Overview of retention-optimization skill
What retention-optimization does
The retention-optimization skill helps you diagnose why users do not come back and turn that into a prioritized plan for improving retention, engagement, and lifetime value. It is best for Product Management work where you need a practical retention-optimization guide, not a generic growth brainstorm.
Who should use it
Use this retention-optimization skill if you manage a mobile app, consumer product, subscription product, or any experience with repeat usage. It is especially useful when the question is “why are users leaving?” or “what should we change first to improve Day 1, Day 7, and Day 30 retention?”
What makes it different
The repo is opinionated about the first inputs it needs: retention metrics, app category, monetization model, and current engagement features. That makes the skill more decision-useful than a broad prompt because it forces benchmark context before recommending fixes. It also points users to app-marketing-context.md, which is a clue that the best results come from product and acquisition context together.
How to Use retention-optimization skill
Install and activation context
Use the retention-optimization install flow with the repository path Eronred/aso-skills and the skill slug retention-optimization. In practice, the skill is meant to be invoked when a user is asking for a retention strategy, churn diagnosis, or a prioritized engagement plan.
What to provide before asking
Give the skill concrete inputs instead of a vague “improve retention” request. The strongest minimum brief includes:
- current Day 1, Day 7, and Day 30 retention
- app category or product type
- monetization model
- current engagement mechanics such as push, streaks, reminders, or community
- the main symptom, such as drop-off after signup, uninstall after first session, or low weekly return rate
A stronger prompt looks like: “We are a subscription productivity app. Day 1 is 18%, Day 7 is 9%, Day 30 is 4%. Most users finish onboarding but do not complete a second task. We use email but no push. Diagnose the likely retention bottlenecks and give a prioritized retention-optimization plan.”
Files to read first
Start with SKILL.md, since it contains the initial assessment flow and benchmark framing. If you are adapting the retention-optimization skill to your own workflow, also inspect any linked context file such as app-marketing-context.md before changing the recommendations. If your install exposes only one file, that is a sign the skill is intentionally lightweight and prompt-driven.
How to turn a rough goal into a usable prompt
Translate “increase retention” into a product question with constraints. State the user segment, lifecycle stage, and what changed recently. Include what you already tried, because the skill’s output is most useful when it can separate diagnosis from obvious fixes. For retention-optimization for Product Management, that usually means asking for a ranked set of actions, the expected retention lever for each action, and the assumptions behind the recommendation.
retention-optimization skill FAQ
Is retention-optimization only for mobile apps?
No, but it is most clearly designed for app retention and engagement strategy. If you are working on SaaS, marketplaces, or content products, the skill can still help if you translate the problem into repeat-use behavior and supply equivalent retention metrics.
How is this different from a normal prompt?
A normal prompt often jumps straight to ideas. The retention-optimization skill first asks for category benchmarks and monetization context, which reduces bad comparisons and generic advice. That is valuable when the real problem is not “more features” but a mismatch between product value, habit formation, and user expectations.
When should I not use it?
Do not use this retention-optimization skill if your problem is primarily acquisition, pricing, or one-time onboarding copy. The repository itself separates onboarding-specific issues from retention, so use it when the user has already reached the product and you need them to return.
Is it beginner-friendly?
Yes, if you can answer a few product questions. It is beginner-friendly because the workflow is structured around clear inputs, but you still need to know the product’s category, metrics, and current engagement setup to get useful output.
How to Improve retention-optimization skill
Give benchmarkable inputs
The biggest quality gain comes from giving the exact retention window and product category. A weak input like “retention is bad” produces generic fixes. A strong input like “D1 22%, D7 8%, D30 3% for a fitness app” lets the skill compare against realistic expectations and prioritize the right problem.
Surface the real drop-off point
Tell the skill where users disappear: after install, after signup, after first task, after week one, or after a billing event. The retention-optimization skill works best when you identify the stage that breaks the habit loop, because the same retention score can come from very different failures.
Iterate on the first plan
After the first answer, refine by asking for one of three follow-ups: the top diagnostic hypothesis, the smallest test worth running, or the engagement feature most likely to move the target cohort. That keeps the retention-optimization guide actionable instead of turning into a broad list of ideas.
Watch for common failure modes
The most common mistake is asking for “retention ideas” without context. Another is mixing retention with monetization or onboarding and expecting one recommendation to solve all three. If the first output feels shallow, add segmentation, recent product changes, and what engagement features already exist before rerunning the skill.