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
Product Analytics
Product Analytics is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to decide the next budget, product,…
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What this Skill does
You need operating data to decide the next budget, product, or channel investment. Bring Metric framework selection and KPI definition for pre-PMF, growth, or mature… and Cohort retention analysis comparing curve shapes across signup or feature-exposure… into the same operating… Start with a small test around “Metric framework selection and KPI definition for pre-PMF, growth, or mature products”, then check whether “Cohort retention analysis comparing curve shapes across signup or feature-exposure cohorts” fits the way your team actually works.
What makes it different
Before you use it
Source file or structured data
This instruction refers to file or tabular input. Prepare the requested file and confirm that the model you use can read 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 product analytics expert. Select framework: AARRR for growth loops, North Star for alignment, HEART for UX quality. Define stage KPIs — pre-PMF: activation and early retention; growth: acquisition efficiency and conversion velocity; mature: retention depth and revenue quality. Design three dashboard layers: Executive (5-7 metrics), Product Health (acquisition, activation, retention, engagement), Feature (adoption, depth, repeat usage). Run cohort analysis comparing retention curves across signup cohorts — identify inflection points at onboarding. Use metrics_calculator.py for retention, cohort matrix, funnel analysis. Every KPI needs target, threshold, owner, and decision rule. Never report single-point retention or averaged-across-segments metrics.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 Product Analytics can help you turn store, pricing, and performance data into operating decisions. Use this when the input boundary, owner, and one primary measure from data completeness, calculation accuracy, contribution margin, and decision follow-through 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 Product Analytics can help you turn store, pricing, and performance data into operating decisions. Use this when the input boundary, owner, and one primary measure from data completeness, calculation accuracy, contribution margin, and decision follow-through 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 Product Analytics can help you turn store, pricing, and performance data into operating decisions.
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 consistent exports, metric definitions, cost data, date ranges, and known data gaps 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 consistent exports, metric definitions, cost data, date ranges, and known data gaps 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 consistent exports, metric definitions, cost data, date ranges, and known data gaps 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 Product Analytics to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a decision-ready analysis 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 Product Analytics to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a decision-ready analysis 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 Product Analytics 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 data completeness, calculation accuracy, contribution margin, and decision follow-through 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 data completeness, calculation accuracy, contribution margin, and decision follow-through 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.
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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