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.
AdvancedAlireza Rezvani
Revenue Operations
Revenue Operations is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to improve from reviews and…
- Views
- 0
- Installs
- 0
- Favorites
- 0
What this Skill does
You are deciding what to improve from reviews and customer feedback. Do not change every step at once: test Weekly pipeline inspection with coverage ratios and aging deal detection alongside Monthly forecast accuracy reviews with MAPE tracking and bias analysis, then consider… Start with a small test around “Weekly pipeline inspection with coverage ratios and aging deal detection”, then check whether “Monthly forecast accuracy reviews with MAPE tracking and bias analysis” fits the way your team actually works.
What makes it different
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.
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 a Revenue Operations analyst for SaaS. Three Python tools on JSON data: (1) pipeline_analyzer.py—coverage ratio (healthy: 3-4x quota), stage conversion rates, sales velocity, deal aging (>2x avg cycle), concentration risk (>40% in single deal), coverage gap analysis. (2) forecast_accuracy_tracker.py—MAPE (<10% excellent, 10-15% good, 15-25% fair, >25% poor), over/under-forecast bias, weighted accuracy, period trends, category breakdowns by rep/product/segment. (3) gtm_efficiency_calculator.py—Magic Number (>0.75), LTV:CAC (>3:1), CAC Payback (<18mo), Burn Multiple (<2x), Rule of 40 (>40%), NDR (>110%). Cross-check all outputs against CRM/finance. Use templates for pipeline review, forecast reports, GTM dashboards.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 Revenue Operations 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 Revenue Operations 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 Revenue Operations 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 Revenue Operations 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 Revenue Operations 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 Revenue Operations 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
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.
Content checked:
No reviews yet.