Churn Prevention workflow skill. Use this skill when the user needs Reduce voluntary and involuntary churn with cancel flows, save offers, dunning, win-back tactics, and retention strategy. Use when users are cancelling, failed payments are rising, or subscription retention needs improvement and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura
name
churn-prevention
description
Churn Prevention workflow skill. Use this skill when the user needs Reduce voluntary and involuntary churn with cancel flows, save offers, dunning, win-back tactics, and retention strategy. Use when users are cancelling, failed payments are rising, or subscription retention needs improvement and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/churn-prevention from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Churn Prevention You are an expert in SaaS retention and churn prevention. Your goal is to help reduce both voluntary churn (customers choosing to cancel) and involuntary churn (failed payments) through well-designed cancel flows, dynamic save offers, proactive retention, and dunning strategies.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Before Starting, How This Skill Works, Cancel Flow Design, Churn Prediction & Proactive Retention, Involuntary Churn: Payment Recovery, Metrics & Measurement.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Use when churn is rising or cancellation behavior needs intervention.
Use when designing cancel flows, save offers, dunning, or retention programs.
Use when the user wants to reduce either voluntary or involuntary churn.
Use when the request clearly matches the imported source intent: Reduce voluntary and involuntary churn with cancel flows, save offers, dunning, win-back tactics, and retention strategy. Use when users are cancelling, failed payments are rising, or subscription retention needs....
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Use when provenance needs to stay visible in the answer, PR, or review packet.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
references/cancel-flow-patterns.md
Starts with the smallest copied file that materially changes execution
Supporting context
references/dunning-playbook.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: Before Starting
Check for product marketing context first:
If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Gather this context (ask if not provided):
1. Current Churn Situation
What's your monthly churn rate? (Voluntary vs. involuntary if known)
How many active subscribers?
What's the average MRR per customer?
Do you have a cancel flow today, or does cancel happen instantly?
2. Billing & Platform
What billing provider? (Stripe, Chargebee, Paddle, Recurly, Braintree)
Monthly, annual, or both billing intervals?
Do you support plan pausing or downgrades?
Any existing retention tooling? (Churnkey, ProsperStack, Raaft)
3. Product & Usage Data
Do you track feature usage per user?
Can you identify engagement drop-offs?
Do you have cancellation reason data from past churns?
What's your activation metric? (What do retained users do that churned users don't?)
Brand tone for offboarding? (Empathetic, direct, playful)
Examples
Example 1: Ask for the upstream workflow directly
Use @churn-prevention to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @churn-prevention against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @churn-prevention for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @churn-prevention using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/churn-prevention, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Imported Troubleshooting Notes
Imported: Common Mistakes
No cancel flow at all — Instant cancel leaves money on the table. Even a simple survey + one offer saves 10-15%
Making cancellation hard to find — Hidden cancel buttons breed resentment and bad reviews. Many jurisdictions require easy cancellation (FTC Click-to-Cancel rule)
Same offer for every reason — A blanket discount doesn't address "missing feature" or "not using it"
Discounts too deep — 50%+ discounts train customers to cancel-and-return for deals
Ignoring involuntary churn — Often 30-50% of total churn and the easiest to fix
No dunning emails — Letting payment failures silently cancel accounts
Guilt-trip copy — "Are you sure you want to abandon us?" damages brand trust
Not tracking save offer LTV — A "saved" customer who churns 30 days later wasn't really saved
Pausing too long — Pauses beyond 3 months rarely reactivate. Set limits.
No post-cancel path — Make reactivation easy and trigger win-back emails, because some churned users will want to come back
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/cancel-flow-patterns.md
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
The exit survey is the foundation. Good reason categories:
Reason
What It Tells You
Too expensive
Price sensitivity, may respond to discount or downgrade
Not using it enough
Low engagement, may respond to pause or onboarding help
Missing a feature
Product gap, show roadmap or workaround
Switching to competitor
Competitive pressure, understand what they offer
Technical issues / bugs
Product quality, escalate to support
Temporary / seasonal need
Usage pattern, offer pause
Business closed / changed
Unavoidable, learn and let go gracefully
Other
Catch-all, include free text field
Survey best practices:
1 question, single-select with optional free text
5-8 reason options max (avoid decision fatigue)
Put most common reasons first (review data quarterly)
Don't make it feel like a guilt trip
"Help us improve" framing works better than "Why are you leaving?"
Dynamic Save Offers
The key insight: match the offer to the reason. A discount won't save someone who isn't using the product. A feature roadmap won't save someone who can't afford it.
Offer-to-reason mapping:
Cancel Reason
Primary Offer
Fallback Offer
Too expensive
Discount (20-30% for 2-3 months)
Downgrade to lower plan
Not using it enough
Pause (1-3 months)
Free onboarding session
Missing feature
Roadmap preview + timeline
Workaround guide
Switching to competitor
Competitive comparison + discount
Feedback session
Technical issues
Escalate to support immediately
Credit + priority fix
Temporary / seasonal
Pause subscription
Downgrade temporarily
Business closed
Skip offer (respect the situation)
—
Save Offer Types
Discount
20-30% off for 2-3 months is the sweet spot
Avoid 50%+ discounts (trains customers to cancel for deals)
Time-limit the offer ("This offer expires when you leave this page")
Show the dollar amount saved, not just the percentage
Pause subscription
1-3 month pause maximum (longer pauses rarely reactivate)
60-80% of pausers eventually return to active
Auto-reactivation with advance notice email
Keep their data and settings intact
Plan downgrade
Offer a lower tier instead of full cancellation
Show what they keep vs. what they lose
Position as "right-size your plan" not "downgrade"
Easy path back up when ready
Feature unlock / extension
Unlock a premium feature they haven't tried
Extend trial of a higher tier
Works best for "not getting enough value" reasons
Personal outreach
For high-value accounts (top 10-20% by MRR)
Route to customer success for a call
Personal email from founder for smaller companies
Cancel Flow UI Patterns
┌─────────────────────────────────────┐
│ We're sorry to see you go │
│ │
│ What's the main reason you're │
│ cancelling? │
│ │
│ ○ Too expensive │
│ ○ Not using it enough │
│ ○ Missing a feature I need │
│ ○ Switching to another tool │
│ ○ Technical issues │
│ ○ Temporary / don't need right now │
│ ○ Other: [____________] │
│ │
│ [Continue] │
│ [Never mind, keep my subscription] │
└─────────────────────────────────────┘
↓ (selects "Too expensive")
┌─────────────────────────────────────┐
│ What if we could help? │
│ │
│ We'd love to keep you. Here's a │
│ special offer: │
│ │
│ ┌───────────────────────────────┐ │
│ │ 25% off for the next 3 months│ │
│ │ Save $XX/month │ │
│ │ │ │
│ │ [Accept Offer] │ │
│ └───────────────────────────────┘ │
│ │
│ Or switch to [Basic Plan] at │
│ $X/month → │
│ │
│ [No thanks, continue cancelling] │
└─────────────────────────────────────┘
UI principles:
Keep the "continue cancelling" option visible (no dark patterns)
One primary offer + one fallback, not a wall of options
Show specific dollar savings, not abstract percentages
Use the customer's name and account data when possible
Mobile-friendly (many cancellations happen on mobile)
The best save happens before the customer ever clicks "Cancel."
Risk Signals
Track these leading indicators of churn:
Signal
Risk Level
Timeframe
Login frequency drops 50%+
High
2-4 weeks before cancel
Key feature usage stops
High
1-3 weeks before cancel
Support tickets spike then stop
High
1-2 weeks before cancel
Email open rates decline
Medium
2-6 weeks before cancel
Billing page visits increase
High
Days before cancel
Team seats removed
High
1-2 weeks before cancel
Data export initiated
Critical
Days before cancel
NPS score drops below 6
Medium
1-3 months before cancel
Health Score Model
Build a simple health score (0-100) from weighted signals:
Health Score = (
Login frequency score × 0.30 +
Feature usage score × 0.25 +
Support sentiment × 0.15 +
Billing health × 0.15 +
Engagement score × 0.15
)
Score
Status
Action
80-100
Healthy
Upsell opportunities
60-79
Needs attention
Proactive check-in
40-59
At risk
Intervention campaign
0-39
Critical
Personal outreach
Proactive Interventions
Before they think about cancelling:
Trigger
Intervention
Usage drop >50% for 2 weeks
"We noticed you haven't used [feature]. Need help?" email
Approaching plan limit
Upgrade nudge (not a wall — paywall-upgrade-cro handles this)
No login for 14 days
Re-engagement email with recent product updates
NPS detractor (0-6)
Personal follow-up within 24 hours
Support ticket unresolved >48h
Escalation + proactive status update
Annual renewal in 30 days
Value recap email + renewal confirmation
Imported: Involuntary Churn: Payment Recovery
Failed payments cause 30-50% of all churn but are the most recoverable.
The Dunning Stack
Pre-dunning → Smart retry → Dunning emails → Grace period → Hard cancel
Pre-Dunning (Prevent Failures)
Card expiry alerts: Email 30, 15, and 7 days before card expires
Backup payment method: Prompt for a second payment method at signup
Card updater services: Visa/Mastercard auto-update programs (reduces hard declines 30-50%)
Pre-billing notification: Email 3-5 days before charge for annual plans
Smart Retry Logic
Not all failures are the same. Retry strategy by decline type:
Decline Type
Examples
Retry Strategy
Soft decline (temporary)
Insufficient funds, processor timeout
Retry 3-5 times over 7-10 days
Hard decline (permanent)
Card stolen, account closed
Don't retry — ask for new card
Authentication required
3D Secure, SCA
Send customer to update payment
Retry timing best practices:
Retry 1: 24 hours after failure
Retry 2: 3 days after failure
Retry 3: 5 days after failure
Retry 4: 7 days after failure (with dunning email escalation)
After 4 retries: Hard cancel with reactivation path
Smart retry tip: Retry on the day of the month the payment originally succeeded (if Day 1 worked before, retry on Day 1). Stripe Smart Retries handles this automatically.
Dunning Email Sequence
Email
Timing
Tone
Content
1
Day 0 (failure)
Friendly alert
"Your payment didn't go through. Update your card."
2
Day 3
Helpful reminder
"Quick reminder — update your payment to keep access."
3
Day 7
Urgency
"Your account will be paused in 3 days. Update now."
4
Day 10
Final warning
"Last chance to keep your account active."
Dunning email best practices:
Direct link to payment update page (no login required if possible)
Show what they'll lose (their data, their team's access)
Don't blame ("your payment failed" not "you failed to pay")
Include support contact for help
Plain text performs better than designed emails for dunning
Acquisition channel — Which channels bring stickier customers?
Plan type — Which plans churn most?
Tenure — When do most cancellations happen? (30, 60, 90 days?)
Cancel reason — Which reasons are growing?
Save offer type — Which offers work best for which segments?
Cancel Flow A/B Tests
Test one variable at a time:
Test
Hypothesis
Metric
Discount % (20% vs 30%)
Higher discount saves more
Save rate, LTV impact
Pause duration (1 vs 3 months)
Longer pause increases return rate
Reactivation rate
Survey placement (before vs after offer)
Survey-first personalizes offers
Save rate
Offer presentation (modal vs full page)
Full page gets more attention
Save rate
Copy tone (empathetic vs direct)
Empathetic reduces friction
Save rate
How to run cancel flow experiments: Use the ab-test-setup skill to design statistically rigorous tests. PostHog is a good fit for cancel flow experiments — its feature flags can split users into different flows server-side, and its funnel analytics track each step of the cancel flow (survey → offer → accept/decline → confirm).
Imported: Tool Integrations
For implementation, use the billing, analytics, and experimentation tools available in the current environment.
