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Agent Skill · 研究检索

churn-prediction(流失预测)

churn-prediction 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

churn-prediction 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,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 36,647。

总安装 1,886周安装 81GitHub Stars 85下载 661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
unknown

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:churn-prediction(流失预测)
来源仓库:https://github.com/guia-matthieu/clawfu-skills
仓库路径:skills/churn-prediction
安装命令:
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill 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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill 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。

用于通过行为模式、支持互动和关系健康度预测客户流失风险。基于 Lincoln Murphy 和 ProfitWell 方法论,分析使用衰减、支持情绪等关键信号。使用时需结合产品使用日志、支付记录和客户沟通数据进行综合评估。通过 GitHub 安装,需确认数据来源权限,避免对未授权数据执行分析操作。

churn-prediction 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/churn-prediction

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/guia-matthieu/clawfu-skills --skill churn-pred…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

churn-prediction 怎么安装?

在终端执行 npx skills add https://github.com/guia-matthieu/clawfu-skills --skill churn-prediction。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

churn-prediction 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

churn-prediction 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

churn-prediction 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

churn-prediction 用的人多吗?

公开数据是累计安装 1,886 次,近一周安装 81 次,下载 661 次,GitHub 85 星。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。

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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
churn-prediction

流失预测 · 本页

1,88685只读未明确通过

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

Churn Prediction

Detect early warning signals of customer churn through systematic analysis of usage patterns, support interactions, and relationship health.

When to Use This Skill

  • Monthly/quarterly churn risk reviews
  • Prioritizing CSM intervention
  • Building early warning systems
  • Post-mortem analysis on lost customers
  • Executive churn reporting

Methodology Foundation

Based on Lincoln Murphy's Churn Analysis and ProfitWell Retention Research, analyzing:

  • Product engagement decay
  • Support sentiment trends
  • Payment behavior changes
  • Relationship deterioration
  • Competitive signals

What Claude Does vs What You Decide

Claude DoesYou Decide
Identifies risk signalsSave vs. let go decisions
Calculates risk scoresResource allocation
Suggests interventionsDiscount/concession offers
Prioritizes at-risk accountsExecutive escalation timing
Analyzes churn patternsRetention strategy changes

What This Skill Does

  1. Signal detection - Identify behavioral indicators of churn risk
  2. Risk scoring - Calculate churn probability
  3. Root cause analysis - Why are they likely to leave?
  4. Intervention planning - What actions could save them?
  5. Pattern recognition - Learn from past churned accounts

How to Use

Assess churn risk for this customer:

Account: [Company Name]
Contract: $[ARR], Renewal: [Date]
Tenure: [Months]

Usage Signals:
- Login frequency: [trend]
- Feature adoption: [% and trend]
- Active users: [current vs licensed]
- Key feature usage: [specific metrics]

Support Signals:
- Recent tickets: [count and nature]
- CSAT trend: [improving/stable/declining]
- Escalations: [any open or recent]
- Sentiment: [last few interactions]

Relationship Signals:
- Champion status: [engaged/disengaged/left]
- Exec sponsor: [status]
- NPS response: [score and comments]
- QBR attendance: [pattern]

Financial Signals:
- Payment status: [current/late]
- Contract discussions: [any mentions of changes]
- Competitor mentions: [any signals]

Instructions

Step 1: Evaluate Leading Indicators

30-60 Day Warning Signs:

SignalRisk LevelWeight
Login drop >50%High15
Feature usage stoppedHigh15
Support tickets spikeMedium10
Champion leftCritical20
Negative NPSHigh12
Payment lateMedium8
No QBR attendanceMedium8
Competitor mentionedHigh12

Step 2: Calculate Churn Probability

Risk Score Formula:

Churn Risk = Sum of weighted signals / 100

Score Ranges:
- 0-20: Low Risk (normal attention)
- 21-40: Moderate Risk (proactive outreach)
- 41-60: High Risk (intervention required)
- 61-80: Critical Risk (executive escalation)
- 81-100: Imminent Churn (save or plan exit)

Step 3: Identify Root Cause Category

CategoryIndicatorsTypical Save Rate
Product FitLow adoption, wrong use case30%
Value GapNot seeing ROI, budget pressure45%
Service IssueSupport failures, unresolved bugs60%
RelationshipChampion left, no engagement35%
CompetitionActively evaluating others25%
Business ChangeM&A, budget cuts, pivot15%

Step 4: Prescribe Intervention

By Root Cause:

CausePrimary ActionSecondary Action
Product FitSuccess planningRight-size contract
Value GapROI reviewExecutive sponsor call
Service IssueEscalation + resolutionService credits
RelationshipNew champion devExecutive mapping
CompetitionCompetitive defensePricing review
BusinessFlexible termsPause option

Step 5: Create Save Plan

90-Day Save Framework:

  • Days 1-7: Triage and stabilize
  • Days 8-30: Address root cause
  • Days 31-60: Rebuild value perception
  • Days 61-90: Secure commitment

Examples

Example 1: High-Risk Account

Input:

Account: MediaTech Corp
Contract: $96K ARR, Renewal: April 15 (45 days)
Tenure: 18 months

Usage Signals:
- Logins: Down 65% last 30 days
- Feature adoption: 35% (was 60%)
- Active users: 8 of 25 licensed
- Core feature: Stopped using analytics module

Support Signals:
- Tickets: 8 this month (normally 2)
- CSAT: Dropped from 4.5 to 3.2
- Escalation: 1 open (data export issue)
- Sentiment: Last 3 interactions negative

Relationship Signals:
- Champion: VP Marketing left 6 weeks ago
- Exec sponsor: Never established
- NPS: 4 (was 8 six months ago)
- QBR: Cancelled last two

Financial Signals:
- Payment: Current
- Mentions: "Evaluating options" in last call
- Competitor: Saw CompetitorX demo on their calendar

Output:

## Churn Risk Assessment: MediaTech Corp

### 🔴 CRITICAL RISK - 78/100

**Churn Probability: 75-85%**
**Time to Action: Immediate**

---

### Risk Signal Analysis

| Signal | Finding | Points |
|--------|---------|--------|
| Usage drop 65% | Critical | 15/15 |
| Feature stopped | Analytics abandoned | 15/15 |
| Champion left | No replacement | 20/20 |
| NPS Detractor | Dropped 4 points | 12/12 |
| Competitor eval | Demo scheduled | 12/12 |
| No QBR | 2 cancelled | 8/8 |
| **Total** | | **78/100** |

### Root Cause Analysis

**Primary: Relationship Void** (Champion departure)
- VP Marketing left 6 weeks ago
- No executive sponsor ever established
- Organizational knowledge lost
- No one internally championing value

**Secondary: Value Gap** (Product not delivering)
- Usage collapse suggests not getting value
- Analytics module abandoned = key feature unused
- May have been champion-dependent usage

**Tertiary: Active Competition**
- CompetitorX demo on calendar
- "Evaluating options" language
- Classic exit signals

### Competitor Intelligence

**CompetitorX Positioning:**
- Likely pitching: Simpler interface, lower price
- Their weakness: Less robust analytics
- Our defense: Depth of features, integration

### 90-Day Save Plan

**WEEK 1: Emergency Triage**

| Day | Action | Owner |
|-----|--------|-------|
| 1 | Resolve data export escalation | Support |
| 1 | CSM call to understand situation | CSM |
| 2 | Identify new potential champion | CSM |
| 3 | VP CS call to express commitment | VP CS |
| 5 | Executive sponsor outreach (their CMO) | CRO |

**WEEK 2-4: Stabilize**
- [ ] Close escalation completely
- [ ] Meet new champion (find one!)
- [ ] Re-onboard inactive users
- [ ] Document value delivered to date
- [ ] Competitive battle card preparation

**WEEK 5-8: Rebuild Value**
- [ ] ROI review presentation
- [ ] Success planning session
- [ ] Showcase new features
- [ ] Get 3 quick wins documented

**WEEK 9-12: Secure Renewal**
- [ ] QBR with exec attendance
- [ ] Renewal discussion
- [ ] Multi-year incentive if appropriate
- [ ] Reference/case study request (confidence signal)

### Save Probability Assessment

| If We... | Save Probability |
|----------|------------------|
| Do nothing | 15% |
| Standard outreach | 25% |
| Execute full plan | 45% |
| Add discount/concession | 55% |
| Exec-to-exec + plan | 60% |

### Decision Point

**Recommended: Full save effort**
- $96K ARR worth 60-90 hours of CSM time
- Cost to acquire replacement: ~$30K
- Reputation risk if churns

**If No Traction by Day 30:**
- Prepare for graceful offboarding
- Offer reduced contract if budget issue
- Maintain relationship for potential return

Example 2: Cohort Churn Analysis

Input:

Analyze churn patterns from these 10 churned accounts:

1. AlphaCo: Left after 8 months, champion left, low usage
2. BetaTech: Left after 14 months, pricing, competitor win
3. GammaCorp: Left after 6 months, wrong fit, never adopted
4. DeltaInc: Left after 24 months, budget cuts, loved product
5. EchoSys: Left after 10 months, support issues, 3 escalations
6. FoxtrotLLC: Left after 18 months, competitor, champion left
7. GolfCo: Left after 4 months, implementation failed
8. HotelGrp: Left after 12 months, didn't see ROI
9. IndiaInc: Left after 9 months, champion left, low NPS
10. JulietCorp: Left after 20 months, M&A, product deprecated

Output:

## Churn Cohort Analysis: Q4 Losses

### Summary
- **Total Churned**: 10 accounts
- **Total ARR Lost**: [Calculate from data]
- **Average Tenure**: 12.5 months

---

### Root Cause Distribution

| Root Cause | Count | % | Avg Tenure |
|------------|-------|---|------------|
| Champion Left | 4 | 40% | 11.3 mo |
| Competitor | 3 | 30% | 17.3 mo |
| Product/Fit | 2 | 20% | 5.0 mo |
| Business Change | 2 | 20% | 22.0 mo |
| Service/Support | 1 | 10% | 10.0 mo |
| Value/ROI | 2 | 20% | 10.0 mo |

*Note: Some accounts had multiple causes*

### Key Insights

**1. Champion Dependency is Critical (40%)**
- 4 of 10 churns involved champion departure
- Average: Churned 3-4 months after champion left
- **Action**: Multi-threading program required

**2. Early Churn = Fit Problem**
- 3 accounts churned <6 months
- All had adoption/implementation issues
- **Action**: Improve qualification + onboarding

**3. Competitor Wins Correlate with Tenure**
- Competitor losses at 14, 18, 20 months
- Long enough to evaluate alternatives
- **Action**: Value reinforcement at 12-month mark

**4. Business Change is Uncontrollable**
- 2 churns from M&A/budget cuts
- Both were "happy" customers
- **Action**: Accept, maintain relationship

### Early Warning Signal Validation

| Signal | Present Before Churn | Lead Time |
|--------|---------------------|-----------|
| Champion left | 4/10 (40%) | 3-4 months |
| Usage drop >40% | 7/10 (70%) | 6-8 weeks |
| NPS drop | 6/10 (60%) | 2-3 months |
| Missed QBR | 5/10 (50%) | 3-4 months |
| Support spike | 3/10 (30%) | 4-6 weeks |

**Best Predictor**: Usage drop >40% (70% correlation)

### Preventability Assessment

| Account | Preventable? | What Would Have Helped |
|---------|--------------|------------------------|
| AlphaCo | Likely | Champion backup plan |
| BetaTech | Possibly | Competitive defense earlier |
| GammaCorp | Unlikely | Better qualification |
| DeltaInc | No | Business change |
| EchoSys | Likely | Faster escalation resolution |
| FoxtrotLLC | Possibly | Multi-thread + compete |
| GolfCo | Likely | Implementation oversight |
| HotelGrp | Likely | Proactive ROI review |
| IndiaInc | Likely | Champion backup |
| JulietCorp | No | M&A out of control |

**Preventability Rate**: 60% (6/10 could have been saved)

### Recommendations

**Process Changes:**
1. Implement champion backup contact rule (2+ contacts)
2. Add 12-month value review to CSM playbook
3. Create competitive defense triggers
4. Improve implementation success metrics

**Investment Areas:**
1. CSM capacity for proactive outreach
2. Competitive intelligence
3. Champion development program

**Metrics to Track:**
- Champion backup coverage %
- Time to first value
- Competitive mention alerts
- 12-month NPS trend

Skill Boundaries

What This Skill Does Well

  • Systematic risk signal analysis
  • Probability scoring with clear logic
  • Root cause categorization
  • Intervention planning

What This Skill Cannot Do

  • Access actual customer data
  • Predict exact churn timing
  • Know internal customer dynamics
  • Replace relationship intuition

When to Escalate to Human

  • Strategic accounts (top 10%)
  • Complex multi-product relationships
  • Escalation requiring legal/exec involvement
  • Pricing concession decisions

Iteration Guide

Follow-up Prompts

  • "Create a 30-60-90 save plan for this account."
  • "What competitive response should we prepare?"
  • "Which of my at-risk accounts should I prioritize?"
  • "Analyze the pattern across all my churned accounts."

Monitoring Cadence

  1. Score all accounts monthly
  2. Alert on score drops >15 points
  3. Weekly review of Critical/High risk
  4. Quarterly pattern analysis

Checklists & Templates

At-Risk Account Checklist

  • Usage trend analyzed (30/60/90 day)
  • Support sentiment reviewed
  • Champion status confirmed
  • NPS collected or requested
  • Competitor signals checked
  • Financial health verified
  • Save plan documented

References

  • Lincoln Murphy's Churn Analysis Framework
  • ProfitWell Retention Benchmarks
  • Gainsight Customer Health Methodology
  • ChurnZero Predictive Analytics

Related Skills

  • account-health - Broader health scoring
  • health-score-monitor - Continuous monitoring
  • renewal-management - Renewal process

Skill Metadata

  • Domain: Customer Success
  • Complexity: Advanced
  • Mode: centaur
  • Time to Value: 20-30 min per account
  • Prerequisites: Customer data, history, context

平台分布

Codex

35.6%
按下载量换算235

Claude

31.13%
按下载量换算206

Cursor

18.87%
按下载量换算125

Gemini CLI

9.32%
按下载量换算62

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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