RD

    留存数据对比 · Retention Data Comparator

    1

    对比不同版本、渠道与人群的留存曲线,输出差异、归因与优化建议。 Compare retention curves across versions, channels, and audiences, with differences, attribution, and optimization advice.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more
    RD

    留存数据对比 · Retention Data Comparator

    Example session with this skill installed

    请对比 3 月与 4 月的 D7/D30 留存,以及付费渠道和自然渠道的留存差异。

    • Read your context and instructions
    • Compiled the retention data comparator

    留存数据对比完成。

    人群D7D30样本结论
    3 月全量32%15%80k基线
    4 月全量35%17%88k提升显著
    付费渠道28%11%30k低于基线
    自然渠道40%20%50k最优

    归因

    • 4 月提升主要来自新引导流程(覆盖 60% 新用户)。
    • 付费渠道首日留存低,疑似素材承诺与产品不符。

    Next steps

    1. 对 4 月新用户按引导版本细分。
    2. 付费素材做承诺对齐 review。
    3. 下周复核 D30 是否维持。

    Connects securely to your tools. The creator never sees your data.

    What you get

    Compare retention curves across different app versions.Benchmark paid vs organic acquisition channel performance.Generate structured retention reports for experienced analysts.Convert raw retention metrics into structured JSON or table formats.

    About this skill

    它解决什么问题

    保留报告比较原始百分比，没有控制群组、渠道或样本大小，因此团队会追逐虚假的获胜者。

    这个技能做什么

    • 跨版本、渠道和细分市场调整保留指标。
    • 对队列大小和时间窗口进行标准化。
    • 突出显示具有统计意义的差异。
    • 生成包含可能原因和后续测试的比较表。

    为什么比裸提示词强

    它强制执行一个比较框架：指标、队列、窗口、增量、置信度、原因，因此保留争论以证据结束。

    典型用例

    • 比较入职变更前后的 D7 保留率。
    • 对付费用户保留率与自然用户保留率进行基准测试。
    • 寻找保留率最高的采集渠道。

    局限

    它会比较您提供的数据；显着性取决于您确认的样本量。

    English · 英文介绍

    The problem

    Retention reports compare raw percentages without controlling for cohorts, channels, or sample size, so teams chase false winners.

    What it does

    • Aligns retention metrics across versions, channels, and segments.
    • Normalizes for cohort size and time windows.
    • Highlights statistically meaningful differences.
    • Produces a comparison table with likely causes and next tests.

    Why this beats prompting it yourself

    It enforces a comparison frame: metric, cohort, window, delta, confidence, cause, so retention debates end with evidence.

    Use cases

    • Comparing D7 retention before and after onboarding changes.
    • Benchmarking paid vs organic user retention.
    • Finding the highest-retention acquisition channel.

    Known limitations

    It compares the data you provide; significance depends on sample sizes you confirm.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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