Includes a retention step after resolution
It ends the workflow with a recovery offer, such as a discount code, after the return outcome has been explained.
IntermediateAlireza Rezvani
Churn Prevention
Churn Prevention is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to you are handling recurring…
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What this Skill does
You are handling recurring pre-sale or post-sale questions. Do not change every step at once: test Designing a cancel flow from scratch for SaaS products with no retention funnel alongside Auditing existing cancel flows for save rate optimization and exit survey…, then consider… Start with a small test around “Designing a cancel flow from scratch for SaaS products with no retention funnel”, then check whether “Auditing existing cancel flows for save rate optimization and exit survey improvements” fits the way your team actually works.
What makes it different
Plans lifecycle emails by customer moment
The instruction separates welcome, abandonment, post-purchase, win-back, and promotional messages into distinct lifecycle moments.
Before you use it
Python runtime
The source includes a Python command or script. A compatible local Python environment is required for that part of the workflow.
No installation needed
Copy and paste into a model chat
- Expand and copy the complete original Skill.md below.
- Open a new conversation in a compatible AI model, then paste it into the chat box.
- Add verified task details, run one low-risk example, and review the result before using it in store operations.
Original Skill.md
The complete source is shown below. Copy it from the top right to use it.
You are a SaaS churn prevention expert. Three modes: (1) Build Cancel Flow—5-stage: Cancel Trigger (visible, no dark patterns), Exit Survey (one required MC question, 6-8 reasons), Dynamic Save Offer (match reason: discount→price objection, pause→seasonal, downgrade→light usage, feature unlock→missing feature, human support→complexity), Confirmation (clear consequences), Post-Cancel (day-0 confirmation, day-7 re-engagement, day-30 win-back). (2) Optimize Existing—audit against benchmarks (save rate 10-15% good, 20%+ excellent; survey completion >80%). (3) Dunning—smart retries (days 3/8/15/18), card updater services, 5-email sequence neutral→urgent. Track save rate, churn rates, recovery rate weekly. Run churn_impact_calculator.py to model MRR recovery.Get started
Starter prompts for the main use cases—copy and use them directly.
01Start with one real task
Do not begin with a store-wide rollout. Pick one reversible task where Churn Prevention can help you handle customer questions, retention signals, and follow-up work. Use this when the input boundary, owner, and one primary measure from resolution quality, reopen rate, response time, and customer satisfaction are written down.
Show prompt and variablesHide prompt and variables
Start with one real task
Do not begin with a store-wide rollout. Pick one reversible task where Churn Prevention can help you handle customer questions, retention signals, and follow-up work. Use this when the input boundary, owner, and one primary measure from resolution quality, reopen rate, response time, and customer satisfaction are written down.
Use the Skill above to help me with this task: Start with one real task.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not begin with a store-wide rollout. Pick one reversible task where Churn Prevention can help you handle customer questions, retention signals, and follow-up work.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
02Prepare the input and guardrails
Collect only the current policies, representative conversations, order context, and escalation rules needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.
Show prompt and variablesHide prompt and variables
Prepare the input and guardrails
Collect only the current policies, representative conversations, order context, and escalation rules needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.
Use the Skill above to help me with this task: Prepare the input and guardrails.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Collect only the current policies, representative conversations, order context, and escalation rules needed for this test. Remove unrelated personal data and state which actions must never run automatically.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
03Inspect the source Skill, then run it
Read the source, installation method, and permission notes before adding Churn Prevention to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a customer-service or retention workflow that a responsible operator can inspect, and it stayed inside the approved boundary.
Show prompt and variablesHide prompt and variables
Inspect the source Skill, then run it
Read the source, installation method, and permission notes before adding Churn Prevention to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a customer-service or retention workflow that a responsible operator can inspect, and it stayed inside the approved boundary.
Use the Skill above to help me with this task: Inspect the source Skill, then run it.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Read the source, installation method, and permission notes before adding Churn Prevention to a separate test project. Keep commands and Skill text exactly as published.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
04Review it against a baseline
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when resolution quality, reopen rate, response time, and customer satisfaction has a pre-test baseline, and errors and exceptions are logged separately.
Show prompt and variablesHide prompt and variables
Review it against a baseline
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when resolution quality, reopen rate, response time, and customer satisfaction has a pre-test baseline, and errors and exceptions are logged separately.
Use the Skill above to help me with this task: Review it against a baseline.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
Risks and operating notes
Only offer approved recovery incentives
The instruction may suggest an exchange, store credit, or discount code. Confirm the offer amount, eligibility, stacking rules, and margin impact before it is sent to a customer.
Use the minimum customer data needed
The workflow may need an order reference or case facts. Do not paste payment details, full addresses, or unrelated order history into a model conversation; redact them unless they are essential to the decision.
Verify commercial terms before acting
Any price, discount, cost, or margin recommendation is only as current as the values you provide. Recheck live prices, tax, shipping, and margin rules before publishing or sending an offer.
Alireza Rezvani
HealthTech CTO and open-source maintainer focused on applied AI, agentic coding, and practical skills for product, research, growth, and operations teams.
Risk, permissions, and limitations
Review third-party permission scopes before providing store data. Never paste payment credentials, customer passwords, or unnecessary personal data into a model. Outputs must be checked by the operator responsible for the workflow.
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