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Agent Skill · 研究检索

mova-churn-prediction(Mova 流失预测)

mova-churn-prediction 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

mova-churn-prediction 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 17,296。

总安装 6,683周安装 273GitHub Stars 公开资料未说明下载 2,140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
1.0.1

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:mova-churn-prediction(Mova 流失预测)
来源仓库:https://github.com/mova-compact/mova-churn-prediction
安装命令:
openclaw skills install mova-churn-prediction
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 mova-churn-prediction

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

预测客户流失概率并制定保留活动策略。适用于客户生命周期管理与留存优化。结合行为信号与人工审批制定干预方案。需输入客户历史数据与交互记录。mova-churn-prediction 属于研究检索类 Skill,可作为该场景下的辅助能力补充。注意模型偏差对特定群体的影响。

适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

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 mova-churn-prediction;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

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

mova-churn-prediction 怎么安装?

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

mova-churn-prediction 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。

mova-churn-prediction 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install mova-churn-prediction 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

mova-churn-prediction 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。

mova-churn-prediction 用的人多吗?

公开数据是累计安装 6,683 次,近一周安装 273 次,下载 2,140 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。

mova-churn-prediction 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

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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。
  • 需要处理研究检索类任务、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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
mova-churn-prediction

Mova 流失预测 · 本页

6,683—执行命令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
mova-churn-prediction
description
Analyze customer behavior signals to predict churn probability and route retention campaign decisions through a human approval gate via MOVA HITL. Trigger when the user asks to predict customer churn, requests a retention analysis, or wants to identify at-risk customers. Human sign-off is required before any targeted retention action is launched.
license
MIT-0
metadata
{"openclaw":{"plugin":{"name":"MOVA","installCmd":"openclaw plugins install openclaw-mova"},"dataSentToExternalServices":[{"service":"MOVA API (api.mova-lab.eu)","data":"segment ID, analysis period, customer behavior features, churn probability scores, model version, human decision, audit metadata"},{"service":"Customer events connector (read-only)","data":"customer activity signals (logins, transactions, support tickets, feature usage) for the specified segment and period"},{"service":"Churn model connector (read-only)","data":"customer feature vectors evaluated by churn prediction model"},{"service":"CRM connector (read-only)","data":"customer profile and segment metadata lookup"}]}}
Contract Skill — A ready-to-use MOVA HITL workflow. Requires the openclaw-mova plugin.

MOVA Churn Prediction

Run an AI churn risk assessment on your customer segment — get a ranked at-risk list with contributing factor breakdown, then route the retention campaign decision through a mandatory human approval gate with a full audit trail.

What it does

  1. Behavior ingestion — customer activity signals (logins, transactions, support tickets, feature usage) for the specified segment and period
  2. Churn model — probability score per customer (0.0–1.0) with contributing factor breakdown
  3. High-risk list — ranked list of at-risk customers above threshold with recommended retention actions
  4. Human gate — customer success manager reviews the list and chooses: launch campaign / launch selective / defer / escalate
  5. Audit receipt — input features, model version, prediction scores, and human approval are all logged

Escalation rules enforced by policy:

  • GDPR check required before any customer is targeted — consent and legitimate interest must be confirmed
  • Model version drift (> 90 days) → recommend review before launch
  • Campaigns above budget threshold → escalate to VP required

Requirements

Plugin: MOVA OpenClaw plugin must be installed in your OpenClaw workspace.

Data flows:

  • Segment ID + period + threshold → api.mova-lab.eu (MOVA platform, EU-hosted)
  • Customer activity data → events connector (read-only, no raw data stored by MOVA)
  • Feature vectors → churn model connector (inference only, read-only)
  • Customer profiles → CRM connector (read-only)
  • Audit journal → MOVA R2 storage, signed
  • No data sent to third parties beyond the above

Demo

Step 1 — Segment submitted: SEG-ENTERPRISE, 30 days, threshold 0.70 Step 1

Step 2 — AI analysis: 300 at-risk customers, avg score 0.75, top signals and findings Step 2

Step 3 — Decision recorded: launch_selective top 10 by churn score + audit receipt Step 3

Quick start

Say "run churn analysis for segment SEG-ENTERPRISE over the last 30 days":

segment_id: SEG-ENTERPRISE
period_days: 30
threshold: 0.70
requestor_id: EMP-0441

The agent fetches behavior signals, scores churn probability per customer, shows the ranked at-risk list with top contributing factors, then asks for your retention decision.

Why contract execution matters

  • GDPR compliance built in — policy enforces consent check before any customer is targeted, not left to the agent's discretion
  • Model version tracking — the exact model version used for scoring is locked in the audit trail, enabling reproducibility audits
  • Immutable decision record — when a customer asks "why did I receive this offer?" or an auditor asks "who approved this campaign?" — the answer is in the system
  • EU AI Act / GDPR Article 22 ready — automated profiling for targeted campaigns requires documented human oversight

What the user receives

OutputDescription
Customers analyzedTotal in segment
At-risk countAbove threshold
Avg churn scoreAverage probability for at-risk group
Per-customer score0.0–1.0 churn probability
Top contributing factorsFeature breakdown (e.g. login drop, support volume)
Model versionScoring model identifier and date
Recommended retention actionsPer-customer suggested action
Recommended decisionAI-suggested campaign choice
Decision optionslaunch_campaign / launch_selective / defer / escalate
Audit receipt IDPermanent signed record of the campaign decision
Compact journalFull event log: feature pull → scoring → human decision

When to trigger

Activate when the user:

  • Asks to predict churn, run retention analysis, or identify at-risk customers
  • Provides a segment ID or cohort with a date range
  • Sets up a scheduled churn review (weekly / monthly)

Before starting, confirm: "Run churn analysis for segment [SEG-ID] — last [N] days?"

If segment ID or period is missing — ask once.

Step 1 — Submit customer segment for analysis

Call tool mova_hitl_start_churn with:

  • segment_id: customer segment or cohort identifier
  • period_days: lookback period in days (e.g. 30)
  • threshold: minimum churn probability to include in at-risk list (e.g. 0.70)
  • requestor_id: employee ID of the requestor

Step 2 — Show at-risk list and decision options

If status = "waiting_human" — show the churn summary and ask to choose:

Segment:           SEG-ID
Period:            N days
Customers at risk: COUNT  (above THRESHOLD)
Avg churn score:   AVG

Top at-risk customers:
[ID | Name | Score | Top factor]
Recommended action: ACTION ← RECOMMENDED
OptionDescription
launch_campaignLaunch retention campaign for all high-risk customers
launch_selectiveLaunch for top-N only (specify N in reason)
deferDefer to next review cycle
escalateEscalate to VP of Customer Success

Call tool mova_hitl_decide with:

  • contract_id: from the response above — this is ctr-chn-xxxxxxxx, NOT the segment ID
  • option: chosen decision
  • reason: manager reasoning

Step 3 — Show audit receipt

Call tool mova_hitl_audit with contract_id. Call tool mova_hitl_audit_compact with contract_id for the full signed scoring chain.

Connect your real data systems

By default MOVA uses a sandbox mock. To route analysis against your live infrastructure, call mova_list_connectors with keyword: "churn".

Relevant connectors:

Connector IDWhat it covers
connector.analytics.customer_events_v1Customer activity event stream
connector.ml.churn_model_v1Churn prediction model (inference endpoint)
connector.crm.customer_lookup_v1Customer profile and segment metadata

Call mova_register_connector with connector_id, endpoint, optional auth_header and auth_value.

Rules

  • NEVER make HTTP requests manually
  • NEVER invent or simulate churn scores — if a tool call fails, show the exact error
  • Use MOVA plugin tools directly — do NOT use exec or shell
  • CONTRACT_ID is ctr-chn-xxxxxxxx from the mova_hitl_start_churn response — NOT the segment ID

平台分布

OpenClaw

88.53%
按下载量换算1,895

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install mova-churn-prediction 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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