Agent skill

Churn Prevention

by borghei in borghei/Claude-Skills

SaaS churn reduction covering cancel flow design, dynamic save offers, exit survey architecture, dunning sequences, payment recovery, win-back campaigns, and churn impact modeling.

MITAuto-check passedMarketing & SEO

Install Churn Prevention

skills CLI
$ npx skills add borghei/Claude-Skills --skill churn-prevention -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install borghei/Claude-Skills churn-prevention --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/business-growth/churn-prevention .claude/skills/churn-prevention && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
churn-prevention
GitHub stars
886
Token cost
~5.7k tokens
SKILL.md length
2,383 words
Files
4 (incl. scripts)
Skills in repo
354
Repo updated
First seen
Licence
MIT

At a glance

SaaS churn reduction covering cancel flow design, dynamic save offers, exit survey architecture, dunning sequences, payment recovery, win-back campaigns, and churn impact modeling.

  • Works in 8 steps: Cancel Trigger → Exit Survey (Required, 1 Question) → Dynamic Save Offer → …
  • Tasks that involve Referral and retention marketing
  • SKILL.md covers Table of Contents, Clarify First, Initial Assessment and Churn Taxonomy, plus 6 more sections
  • Runs Python scripts from its folder; calls python

What it does

Churn Prevention is an agent skill from borghei/Claude-Skills. SaaS churn reduction covering cancel flow design, dynamic save offers, exit survey architecture, dunning sequences, payment recovery, win-back campaigns, and churn impact modeling.

Its SKILL.md is about 5.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/churn_impact_calculator.py`, `scripts/dunning_sequence_analyzer.py` and `scripts/exit_survey_analyzer.py`).

It sits in Marketing & SEO, covering Referral and retention marketing. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Tasks that involve Referral and retention marketing

Example prompts

  • “/churn-prevention”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Cancel Trigger
  2. Exit Survey (Required, 1 Question)
  3. Dynamic Save Offer
  4. Confirmation
  5. Post-Cancel
  6. churn_impact_calculator.py
  7. dunning_sequence_analyzer.py
  8. exit_survey_analyzer.py

What it can do on your machine

Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Churn Prevention loads about 5.7k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 2,383 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~5.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 2,383 words, ~5,650 tokens.

Download SKILL.mdSave it as .claude/skills/churn-prevention/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
churn-prevention
description
SaaS churn reduction covering cancel flow design, dynamic save offers, exit survey architecture, dunning sequences, payment recovery, win-back campaigns, and churn impact modeling.
license
MIT + Commons Clause
metadata.version
1.0.0
metadata.author
borghei
metadata.category
business-growth
metadata.updated
2026-03-31
metadata.tags
churn, retention, cancel-flow, dunning, payment-recovery, win-back

Churn Prevention

Production-grade SaaS churn reduction framework covering cancel flow architecture, dynamic save offer mapping, exit survey design, dunning sequence engineering, payment recovery optimization, win-back campaigns, and churn impact modeling. Addresses both voluntary churn (customers who decide to leave) and involuntary churn (customers who leave due to payment failure).


Table of Contents


Clarify First

Before designing the churn-prevention system, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Voluntary vs involuntary churn split — which dominates (determines whether to build cancel flow/save offers or dunning/payment recovery)
  • Existing cancel flow vs instant/support cancellation — sets build-from-scratch vs optimize mode
  • Current MRR + ARPU + billing cycle — sizes the dollar impact and the save-offer budget
  • Payment processor — Stripe / Braintree / Paddle / Recurly (determines card-updater and dunning retry implementation)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the deliverable.

Initial Assessment

Required Context
QuestionWhy It Matters
Current monthly churn rate? (voluntary vs involuntary split)Determines which lever to pull
Do you have a cancel flow, or is cancellation instant/via support?Determines build vs optimize mode
What payment processor? (Stripe, Braintree, Paddle)Affects dunning implementation
Average contract value and billing cycle?Sizes the save offer budget
Current MRR?Calculates the dollar impact of churn reduction
SaaS model? (self-serve vs sales-assisted)Determines intervention type
Do you collect exit reasons today?Data availability for save offer mapping

Churn Taxonomy

Voluntary Churn (Customer Decides to Leave)
TypeSignalAddressable?
Value gapNot getting enough value for the priceYes -- save offers, feature education
Product-market mismatchWrong ICP, product does not fit their use casePartially -- downgrade or pivot
Competitor switchFound a better alternativeYes -- competitive counter-offers
Budget cutCannot afford it anymoreYes -- discount or pause
Project completionSeasonal or project-based needYes -- pause option
Poor experienceBad support, bugs, frustrationYes -- human intervention
Never activatedSigned up, never used itPartially -- reactivation before cancel
Involuntary Churn (Payment Fails)
Cause% of Failed PaymentsRecoverable?
Expired card40-50%Yes -- card updater service
Insufficient funds20-30%Yes -- smart retry timing
Bank decline (fraud flag)10-15%Sometimes -- customer must contact bank
Account closed5-10%No -- customer must provide new card
Network error5-10%Yes -- automatic retry

Cancel Flow Architecture

The 5-Stage Cancel Flow
[Cancel Button] → [Exit Survey] → [Dynamic Save Offer] → [Confirmation] → [Post-Cancel]
Stage 1: Cancel Trigger
  • Cancel option is findable (Settings > Account > Cancel). Do not hide it.
  • Clicking "Cancel" starts the flow -- it does not immediately cancel the account
  • Works on both desktop and mobile
Stage 2: Exit Survey (Required, 1 Question)

Question: "What is the main reason you are cancelling?"

Present as radio buttons (not a dropdown). Maximum 8 options:

ReasonInternal Code
Too expensive for the value I getPRICE
Not using it enoughLOW_USAGE
Missing a feature I needMISSING_FEATURE
Switching to a different productCOMPETITOR
My project or need endedPROJECT_END
Too complicated to useCOMPLEXITY
Just testing, did not plan to keep itTESTING
Other (with optional text field)OTHER

Rules:

  • Survey is required before showing the save offer (the answer determines the offer)
  • One question only. No multi-page surveys.
  • Optional free-text field for "Other" and as a supplement to any selection
  • Track response distribution monthly to identify systemic issues
Stage 3: Dynamic Save Offer

Map each exit reason to exactly one save offer:

Exit ReasonSave OfferOffer Copy
PRICE30-50% discount for 2-3 months"We'd like to offer you [X]% off for the next [N] months"
LOW_USAGEPause account for 1-3 months"Pause your account and come back when you need it"
MISSING_FEATURERoadmap preview + workaround"[Feature] is coming in [Q]. Here's how to achieve it now"
COMPETITORCompetitive comparison + discount"Here's how we compare to [competitor]. Plus [X]% off"
PROJECT_ENDPause option"Pause instead of cancel -- your data stays safe"
COMPLEXITYFree onboarding session"Let us set it up for you -- free 30-min session with our team"
TESTINGNo offer -- let them go"Thanks for trying us out. You're welcome back anytime."
OTHERGeneral retention offer"Before you go -- we'd love to make this right. [Contact support]"

Offer presentation rules:

  • One clear offer per screen (not multiple choices)
  • Quantify the value: "Save $120 over the next 3 months" not "Get a discount"
  • CTA: "Accept Offer" vs "Continue Cancelling" (both clearly labeled)
  • No countdown timers, no fake urgency
  • No guilt-trip copy
Stage 4: Confirmation

If they decline the save offer or there is no offer to make:

┌────────────────────────────────────────┐
│  We're sorry to see you go             │
│                                        │
│  What happens when you cancel:         │
│  - Your data is saved for 90 days     │
│  - Access continues until [date]      │
│  - You can reactivate anytime         │
│                                        │
│  [Yes, Cancel My Account]             │
│  [Wait, I Changed My Mind]            │
│                                        │
│  No pre-checked boxes.                │
│  No confusing language.               │
└────────────────────────────────────────┘
Stage 5: Post-Cancel
TimingChannelMessage
ImmediatelyEmailCancellation confirmation + data retention policy + reactivation link
Day 7Email"We miss you" + single CTA to reactivate + what they are missing
Day 30EmailProduct update + relevant improvement + reactivation offer
Day 60EmailFinal win-back with strongest offer (if applicable)

Exit Survey Design

Data Analysis Framework

Track exit survey responses monthly and calculate:

MetricFormulaAction Threshold
Reason distribution% of cancels per reasonAny reason > 30% = systemic issue
Save rate by reasonSaved / Cancel attempts per reasonAny reason < 5% save rate = wrong offer
Reason trendMonth-over-month changeIncreasing trend = worsening problem
Feature gap frequencyCount of "missing feature" with specific feature namedTop 3 missing features = product roadmap input
Competitive Intelligence from Exit Surveys

When users select "Switching to a different product":

  • Ask a follow-up: "Which product are you switching to?" (optional, free text or dropdown)
  • Track the top 3 competitors winning your churners
  • Feed this data into competitive-teardown skill for quarterly analysis

Dynamic Save Offer System

Offer Economics
Offer TypeCost to BusinessSave Rate BenchmarkWhen Profitable
30% discount (3 months)30% of 3 months revenue15-25%If LTV after save > discount cost
50% discount (2 months)50% of 2 months revenue20-30%If retained customer stays 6+ months
Pause (1-3 months)$0 (no revenue during pause)25-40%If 50%+ reactivate after pause
Free onboarding sessionCS team time (~$50-100)10-20%If ARPU > $100/month
Downgrade to lower tierRevenue reduction30-50%If some revenue > no revenue
Feature unlock$0 (already built)5-15%Always profitable
Save Offer Decision Tree
User selects exit reason →
├── PRICE →
│   ├── Customer ARPU > median? → Offer 30% discount
│   └── Customer ARPU < median? → Offer downgrade to cheaper plan
├── LOW_USAGE →
│   ├── Last login > 30 days? → Offer pause
│   └── Last login < 30 days? → Offer usage tips + discount
├── MISSING_FEATURE →
│   ├── Feature on roadmap? → Share roadmap + workaround
│   └── Feature not planned? → Offer discount or acknowledge gap
├── COMPETITOR →
│   ├── Known competitor? → Show comparison + retention offer
│   └── Unknown competitor? → General retention offer
├── PROJECT_END →
│   └── Always → Offer pause
├── COMPLEXITY →
│   ├── Enterprise/high-value? → Offer dedicated onboarding session
│   └── SMB/low-value? → Offer guided tutorial link
└── TESTING →
    └── Always → No offer, let go gracefully

Dunning Sequence Engineering

Failed payments cause 20-40% of total churn. Most of it is recoverable with proper dunning.

Smart Retry Schedule

Do not retry immediately after failure. Cards often recover within 3-7 days.

RetryTimingWhy This Timing
Initial chargeDay 0Normal billing cycle
Retry 1Day 3Most card issues resolve within 72 hours
Retry 2Day 7Paycheck cycle alignment
Retry 3Day 12Second paycheck cycle
Retry 4Day 18Final attempt before service action
Service actionDay 21Downgrade or cancel
Card Updater Services

Enable automatic card updating to prevent expired card churn:

ProcessorServiceHow to Enable
StripeAutomatic card updatesEnabled by default on most plans
BraintreeAccount UpdaterMust enable in merchant settings
PaddleBuilt-inAutomatic
RecurlyAccount UpdaterConfiguration required
Dunning Email Sequence
DaySubject LineBody FocusCTA
0"Your [Product] payment didn't go through"Factual, no blame. Card may be expired or funds unavailable.[Update Payment Method]
3"Action needed: update your payment for [Product]"Remind what they will lose access to.[Update Payment Method]
7"Your [Product] account is at risk"List features/data they have created. Mild urgency.[Update Payment Method]
14"Final notice: your [Product] access ends in 7 days"Clear deadline. Offer to help if bank issue.[Update Payment Method] + [Contact Support]
21"Your [Product] account has been paused"Account status change. Data is safe. Easy reactivation.[Reactivate Account]

Email rules:

  • Every email links directly to the payment update page (not the dashboard)
  • No guilt, no shame. Card failures happen.
  • Subject lines are specific (include product name)
  • Include the amount owed and the card last 4 digits
  • Offer a support channel for customers who need help

Win-Back Campaign Framework

Win-Back Timing
WindowSuccess RateApproach
Day 7 post-cancel5-10%Gentle reminder, no pressure
Day 30 post-cancel3-7%Product update + offer
Day 60 post-cancel2-5%Strongest offer + fresh start
Day 90+ post-cancel1-3%Major product change only
Win-Back Email Sequence

Day 7 Email:

  • Subject: "Your [Product] account is waiting for you"
  • Body: What they left behind (data, projects, team). One CTA: reactivate.
  • No discount. Just value reminder.

Day 30 Email:

  • Subject: "Here's what's new in [Product]"
  • Body: 2-3 specific improvements since they left. One CTA: reactivate.
  • Small incentive: "Come back with 1 month free"

Day 60 Email:

  • Subject: "We'd love to have you back -- [offer]"
  • Body: Strongest offer (50% off 3 months or extended free period). Clear deadline.
  • Final significant outreach attempt.

Show full SKILL.md (951 more words)Show less

Churn Health Scoring

Leading Indicators of Churn
SignalWeightDetection
Login frequency declining (week over week)HighUsage analytics
Feature usage droppingHighFeature event tracking
Support ticket escalationHighHelp desk data
NPS response < 7HighSurvey data
Invoice dispute or payment questionMediumBilling system
Champion left the companyHighContact monitoring
Contract renewal in < 90 daysMediumCRM data
Competitor evaluation detectedHighSales intelligence
Risk Score Calculation
Risk Score = Sum of (Signal Weight x Signal Present)

0-20: Low risk (monitor)
21-40: Moderate risk (proactive outreach)
41-60: High risk (intervention required)
61+: Critical risk (executive escalation)

Metrics and Benchmarks

Key Metrics
MetricFormulaGoodExcellent
Save rateCustomers saved / Cancel attempts10-15%20%+
Voluntary churn rateVoluntary cancels / Total customers (monthly)< 3%< 1.5%
Involuntary churn rateFailed payment cancels / Total customers (monthly)< 1.5%< 0.5%
Payment recovery rateFailed payments recovered / Total failed25-35%40%+
Win-back rateReactivations / Cancellations (90-day window)5-10%10%+
Exit survey completion rateSurveys completed / Cancel attempts> 70%> 90%
Save offer acceptance rateOffers accepted / Offers shown15-25%30%+
Red Flags
SignalDiagnosisAction
Save rate < 5%Offers not matching reasonsRebuild offer-reason mapping
Exit survey completion < 60%Survey too long or optionalMake it required, 1 question
Recovery rate < 20%Retry logic or emails brokenAudit dunning sequence
Single reason > 40%Systemic product/pricing issueEscalate to product/leadership
Churn rate > 5% monthlyBusiness is likely contractingChurn prevention alone will not fix; review ICP + product

Churn Impact Calculator

Quick Estimate
Monthly MRR at risk = Total MRR x Monthly churn rate
Annual MRR saved by 1% churn reduction = Total MRR x 0.01 x 12
Annual MRR saved by 20% save rate = (Monthly MRR at risk x 0.20) x 12

Example:
  MRR: $500,000
  Monthly churn: 4% = $20,000/month lost
  Reduce to 3% = $5,000/month saved = $60,000/year
  Add 20% save rate on remaining = $3,000/month saved = $36,000/year
  Total annual impact: $96,000

Output Artifacts

ArtifactFormatDescription
Cancel Flow Design5-stage flow with copyComplete flow from trigger to post-cancel
Exit SurveyRadio button options + mapping6-8 reasons with save offer mapping
Save Offer SystemDecision treeReason-to-offer mapping with economics
Dunning Sequence5-email sequenceSubject lines, body copy, timing, retry schedule
Win-Back Campaign3-email sequenceDay 7, 30, 60 emails with subject lines and offers
Churn ScorecardMetric tableCurrent metrics vs benchmarks with gap analysis
Impact ModelRevenue calculationDollar impact of churn reduction at various improvement levels

  • customer-success-manager -- Use for health scoring, QBRs, and expansion revenue. Not for cancel flow or dunning design.
  • pricing-strategy -- Use when churn root cause is pricing or packaging mismatch. Not for save offer design.
  • onboarding-cro -- Use when churn traces back to poor activation. If users never experienced value, fix onboarding first.
  • referral-program -- Use for acquisition. Churn prevention handles the other end of the funnel.

Tool Reference

1. churn_impact_calculator.py

Purpose: Calculate the revenue impact of churn reduction at various improvement levels.

bash
python scripts/churn_impact_calculator.py --mrr 500000 --churn-rate 4.0 --save-rate 20
python scripts/churn_impact_calculator.py --mrr 500000 --churn-rate 4.0 --save-rate 20 --json
FlagRequiredDescription
--mrrYesCurrent monthly recurring revenue in dollars
--churn-rateYesCurrent monthly churn rate as percentage (e.g., 4.0 for 4%)
--save-rateNoCancel flow save rate as percentage (default: 15)
--target-churnNoTarget churn rate as percentage (default: current minus 1)
--jsonNoOutput results as JSON
2. dunning_sequence_analyzer.py

Purpose: Analyze dunning email sequence effectiveness and recommend retry timing optimizations.

bash
python scripts/dunning_sequence_analyzer.py dunning_data.json
python scripts/dunning_sequence_analyzer.py dunning_data.json --json
FlagRequiredDescription
dunning_data.jsonYesJSON file with failed payment and retry data
--jsonNoOutput results as JSON

Input JSON format:

json
{
  "failed_payments": [
    {
      "payment_id": "PAY-001",
      "amount": 99.00,
      "failure_reason": "expired_card",
      "retry_attempts": [
        {"day": 0, "recovered": false},
        {"day": 3, "recovered": false},
        {"day": 7, "recovered": true}
      ]
    }
  ]
}
3. exit_survey_analyzer.py

Purpose: Analyze exit survey responses to identify churn patterns, save offer effectiveness, and systemic issues.

bash
python scripts/exit_survey_analyzer.py survey_data.json
python scripts/exit_survey_analyzer.py survey_data.json --json
FlagRequiredDescription
survey_data.jsonYesJSON file with exit survey response data
--jsonNoOutput results as JSON
--periodNoAnalysis period label (default: "current")

Troubleshooting

ProblemLikely CauseSolution
Save rate below 5% across all reasonsSave offers do not match exit reasonsRebuild the exit-reason-to-offer mapping using survey data; run exit_survey_analyzer.py to identify mismatches
Exit survey completion under 60%Survey is optional or too longMake the single-question survey required before showing the save offer; remove multi-page flows
Payment recovery rate below 20%Retry logic misconfigured or dunning emails not sendingAudit dunning sequence with dunning_sequence_analyzer.py; verify email deliverability and retry schedule
Single exit reason exceeds 40% of responsesSystemic product or pricing issueEscalate to product or leadership; this is not solvable with cancel flow alone
Churn rate above 5% monthlyLikely ICP, product-market fit, or pricing problemChurn prevention alone will not fix this; pair with pricing-strategy and product feedback loops
Win-back emails have zero reactivationsEmails not reaching inbox or offers are weakCheck deliverability (SPF, DKIM, DMARC); test stronger offers; verify reactivation links work
Involuntary churn rising while voluntary is stableCard updater not enabled or retry timing is poorEnable automatic card updating on your payment processor; review retry schedule in dunning_sequence_analyzer.py

Success Criteria

  • Monthly voluntary churn rate below 2.5% (below 1.5% is excellent)
  • Monthly involuntary churn rate below 1.0% (below 0.5% is excellent)
  • Cancel flow save rate of 15-25% (above 20% is excellent)
  • Payment recovery rate of 30%+ on failed payments
  • Exit survey completion rate above 80%
  • Win-back reactivation rate of 5-10% within 90 days post-cancel
  • Save offer acceptance rate above 20% with retained customers staying 6+ months post-save

Scope & Limitations

  • In scope: Cancel flow design, exit survey architecture, save offer mapping, dunning sequences, payment recovery, win-back campaigns, churn impact modeling
  • Out of scope: Product-market fit analysis, pricing restructuring, ICP redefinition, customer acquisition
  • Data dependency: Scripts analyze point-in-time snapshots from JSON input; no real-time CRM integration
  • Not predictive ML: All scoring is deterministic and algorithmic -- no machine learning models
  • Legal note: Cancel flows must comply with FTC guidelines (US) and consumer protection laws (EU) -- do not make cancellation unreasonably difficult
  • Revenue estimates: Impact calculations are projections based on input assumptions, not guarantees

Integration Points

  • customer-success-manager -- Feed health scores into churn risk assessment; use churn data to calibrate health score thresholds
  • pricing-strategy -- When exit survey data shows PRICE as the dominant reason (>30%), escalate to pricing-strategy for structural pricing review
  • onboarding-cro -- When exit survey data shows LOW_USAGE or COMPLEXITY as top reasons, the root cause is often poor activation; fix onboarding first
  • revenue-operations -- Pipeline and forecast models should account for churn reduction impact on net revenue retention (NRR)
  • referral-program -- Retained customers from save offers are candidates for referral program enrollment after 90 days of continued usage

© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts) in business-growth/churn-prevention of borghei/Claude-Skills.

  • SKILL.md
  • scripts/churn_impact_calculator.py
  • scripts/dunning_sequence_analyzer.py
  • scripts/exit_survey_analyzer.py

Open the folder on GitHubat commit 4a698e8

Compare with similar skills

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100m Leadsgetagentseal/founder-playbook729—~2.3kAutomated safety check: PassMIT
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Categories

Questions about Churn Prevention

What does Churn Prevention do?

SaaS churn reduction covering cancel flow design, dynamic save offers, exit survey architecture, dunning sequences, payment recovery, win-back campaigns, and churn impact modeling. Churn Prevention is an agent skill from borghei/Claude-Skills. SaaS churn reduction covering cancel flow design, dynamic save offers, exit survey architecture, dunning sequences, payment recovery, win-back campaigns, and churn impact modeling.

When should I use Churn Prevention?

Churn Prevention fits situations like: tasks that involve Referral and retention marketing.

How do I install Churn Prevention in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill churn-prevention -a claude-code`. Or copy the skill folder (business-growth/churn-prevention in borghei/Claude-Skills) into .claude/skills/churn-prevention in your project. Claude Code loads it when a task matches its description.

How do I install Churn Prevention in Codex?

Run `npx skills add borghei/Claude-Skills --skill churn-prevention -a codex`. Or copy the skill folder (business-growth/churn-prevention in borghei/Claude-Skills) into .agents/skills/churn-prevention in your project. Codex loads it when a task matches its description.

Can I use Churn Prevention in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill churn-prevention -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/churn-prevention, .gemini/skills/churn-prevention, .github/skills/churn-prevention and .opencode/skills/churn-prevention in your project.

What does Churn Prevention need to run?

Going by SKILL.md and its folder, Churn Prevention needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Churn Prevention access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Churn Prevention safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Churn Prevention use?

Churn Prevention is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Churn Prevention use?

About 5.7k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Churn Prevention?

Skills that share tags, products or a category with Churn Prevention: Referrals (coreyhaines31/marketingskills, 54k stars), Referral Program (freekmurze/dotfiles, 1k stars), Churn Prevention (rongxinzy/RongxinAI, 154 stars) and 100m Leads (getagentseal/founder-playbook, 729 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Churn Prevention?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 886 GitHub stars. The repository holds 354 skills in this directory. The repository was last updated on October 7, 2026.

Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.