Turns customer feedback into structured output
The source asks the model to group, classify, answer, or prioritize review feedback rather than treating every comment as an isolated case.
BeginnerAlireza Rezvani
Email Sequence
Email Sequence is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to make acquisition, post-purchase, or…
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What this Skill does
You are trying to make acquisition, post-purchase, or win-back outreach more deliberate. Bring Building welcome/onboarding sequences for new trial signups and Creating re-engagement sequences for cold email lists with progressive offers into the same operating path before… Start with a small test around “Building welcome/onboarding sequences for new trial signups”, then check whether “Creating re-engagement sequences for cold email lists with progressive offers” 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.
Structures the analysis before making a recommendation
The source asks for analysis, classification, ranking, or scoring before it reaches a conclusion or next action.
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 an email marketing expert. Before creating any sequence, establish: (1) Type—welcome, nurture, re-engagement, post-purchase, event, educational, sales. (2) Audience—who, what triggered entry, current relationship. (3) Goals—conversion, relationship, segmentation. Deliver Sequence Overview (name, trigger, goal, length, timing, exit conditions) then per-email drafts: Subject, Preview, Body, CTA→destination, segment/conditions. Run sequence_analyzer.py on assembled JSON; fix anything scoring below 70. For 5+ emails, lead with overview table. Include metrics benchmarks, segmentation rules, 3 subject-line variations per email for A/B testing.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 Email Sequence 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 Email Sequence 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 Email Sequence 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 Email Sequence 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 Email Sequence 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 Email Sequence 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
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.
Keep claims tied to verified product facts
When this instruction drafts marketing or product content, review specifications, comparisons, performance claims, and testimonials against approved evidence before publishing.
Treat review patterns as hypotheses to test
The instruction can group and prioritize feedback, but repeated wording is not proof of a product defect or customer-wide preference. Check the underlying sample before changing a product, policy, or campaign.
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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