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Agent Skill · 效率

customer-lifetime-value-optimizer(客户终生价值优化器)

customer-lifetime-value-optimizer 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

customer-lifetime-value-optimizer 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为权限未标注;许可证为 MIT-0,第三方扫描已通过,按安装量在效率类的 21,003 个 Skill 里排第 16,993。

总安装 2,724周安装 117GitHub Stars 公开资料未说明下载 955核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:customer-lifetime-value-optimizer(客户终生价值优化器)
来源仓库:https://github.com/harrylabsj/customer-lifetime-value-optimizer
安装命令:
openclaw skills install customer-lifetime-value-optimizer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install customer-lifetime-value-optimizer

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

customer-lifetime-value-optimizer 基于消费行为细分客户并估算终生价值潜力。适用于 OpenClaw 中设计会员体系、优惠券投放与忠诚度计划策略。输出包含 RFM 模型标签、CLV 区间分布与推荐营销动作优先级排序。订单历史数据质量直接影响估值精度,缺失值需通过插补或排除处理。

促销策略应平衡短期转化与长期利润,避免损害品牌价值的行为激励。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install customer-lifetime-value-optimizer;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

customer-lifetime-value-optimizer 怎么安装?

在终端执行 openclaw skills install customer-lifetime-value-optimizer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

customer-lifetime-value-optimizer 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

customer-lifetime-value-optimizer 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

customer-lifetime-value-optimizer 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

customer-lifetime-value-optimizer 用的人多吗?

公开数据是累计安装 2,724 次,近一周安装 117 次,下载 955 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

customer-lifetime-value-optimizer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
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github

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7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
customer-lifetime-value-optimizer

客户终生价值优化器 · 本页

2,724—权限未标注MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
customer-lifetime-value-optimizer
description
Segment ecommerce customers by repeat behavior, margin quality, membership depth, and churn or return risk, then turn rough order-history notes into a prioritized LTV growth plan. Use when CRM, membership, lifecycle, or retention teams need segment-specific growth actions without live CDP, ESP, or data-warehouse integrations.

Customer Lifetime Value Optimizer

Overview

Use this skill to convert customer-segment notes, order-history summaries, gross-margin signals, and retention context into a practical LTV action plan. It is built for operators who need fast prioritization across new-customer nurture, repeat purchase growth, margin protection, and winback strategy.

This MVP is heuristic. It does not connect to live CRM, CDP, ESP, loyalty, or analytics systems. It relies on the user's segment notes, exported summaries, and lifecycle context.

Trigger

Use this skill when the user wants to:

  • identify which customer segments deserve the most retention investment
  • design different lifecycle moves for high-value, price-sensitive, dormant, or return-risk customers
  • rank LTV levers such as repeat rate, AOV, margin mix, or churn reduction
  • turn rough order-history notes into a CRM or membership action brief
  • separate revenue growth ideas from margin-quality and retention-quality risks

Example prompts

  • "Which segments should we prioritize to improve LTV this quarter?"
  • "Create a retention plan for VIP, new, and dormant customers"
  • "How can we grow LTV without overusing discounts?"
  • "Turn these order and membership notes into an LTV roadmap"

Workflow

  1. Capture the customer segments, order behavior, and whether the main tension is repeat rate, AOV, churn, or margin quality.
  2. Normalize the likely LTV signals: order history, repurchase cycle, segment mix, return behavior, and offer sensitivity.
  3. Separate customer groups into different action lanes instead of giving one generic lifecycle answer.
  4. Rank the highest-value LTV levers and attach practical plays, owners, and success metrics.
  5. Return a markdown plan with segment diagnosis, lever ranking, and action packages.

Inputs

The user can provide any mix of:

  • customer segments or membership tiers
  • order history and repeat-cycle notes
  • AOV, gross margin, bundle rate, or attach-rate context
  • churn, dormancy, or lapsed-customer notes
  • refund or return-risk observations
  • lifecycle messaging constraints and incentive constraints

Outputs

Return a markdown plan with:

  • a segment diagnosis table
  • ranked LTV levers
  • action packages by segment
  • short, medium, and longer-horizon priorities
  • measurement notes, assumptions, and limits

Safety

  • Do not claim access to live CRM, ESP, loyalty, or analytics systems.
  • Do not auto-send discounts, coupons, or lifecycle messages.
  • Keep revenue lift and margin impact separate in the recommendations.
  • Downgrade certainty when user-level order history is incomplete.
  • Treat financial LTV models and operator-facing lifecycle plans as related but not identical.

Best-fit Scenarios

  • CRM and membership planning for ecommerce teams
  • repeat-purchase and lifecycle improvement reviews
  • retention strategy design when data is partial but usable
  • operator-led businesses that need an action plan before building a deeper model

Not Ideal For

  • formal finance-grade LTV forecasting
  • automatic customer scoring or trigger orchestration
  • businesses with no segment or order-history visibility at all
  • scenarios that require privacy-reviewed activation logic

Acceptance Criteria

  • Return markdown text.
  • Include segment diagnosis, lever ranking, action packages, and limits.
  • Show at least one short-term, one medium-term, and one longer-term move.
  • Keep the plan practical for CRM, lifecycle, and retention operators.

平台分布

OpenClaw

97.05%
按下载量换算927

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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