Checks return eligibility and routes outcomes
The source instruction checks policy eligibility, distinguishes final-sale and item-condition cases, then routes the request to refund, store credit, exchange, or escalation.
IntermediateAlireza Rezvani
Market Research
Market Research is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to build a decision-ready market-size…
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What this Skill does
Build a decision-ready market-size and segmentation analysis without relying on one unsupported TAM number. The Skill provides a rigorous method and scripts, but the operator must define the market, choose credible inputs, reconcile conflicting estimates, and connect the result to an ecommerce decision.
What makes it different
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
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 market-research analyst providing upstream methodology. Compute TAM/SAM/SOM by BOTH top-down and bottoms-up methods side-by-side — never return a single unsourced number. Reconcile divergence before quoting. Plan survey sample sizes with finite-population correction and per-segment minimum floors. Score segments against Kotler's five criteria: measurable, substantial, accessible, differentiable, actionable. Drop segments failing substantiality or accessibility gates. Every output carries its method, assumptions, and confidence. Use market_sizer.py with --method both, sample_size_planner.py with --population/--confidence/--moe, and segmentation_scorer.py with the appropriate market profile.Get started
Starter prompts for the main use cases—copy and use them directly.
01Define the market boundary
State exactly who buys what, where, through which channel, and during which period. Use this when two analysts would count the same customers and transactions.
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Define the market boundary
State exactly who buys what, where, through which channel, and during which period. Use this when two analysts would count the same customers and transactions.
Use the Skill above to help me with this task: Define the market boundary.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
State exactly who buys what, where, through which channel, and during which period.
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.
02Run both sizing methods
Calculate top-down and bottom-up estimates with visible formulas, units, assumptions, and source dates. Use this when every number has a source or a named assumption.
Show prompt and variablesHide prompt and variables
Run both sizing methods
Calculate top-down and bottom-up estimates with visible formulas, units, assumptions, and source dates. Use this when every number has a source or a named assumption.
Use the Skill above to help me with this task: Run both sizing methods.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Calculate top-down and bottom-up estimates with visible formulas, units, assumptions, and source dates.
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.
03Reconcile the gap
Explain why the methods differ and revise scope or assumptions before presenting a headline range. Use this when the remaining range is tied to known uncertainty.
Show prompt and variablesHide prompt and variables
Reconcile the gap
Explain why the methods differ and revise scope or assumptions before presenting a headline range. Use this when the remaining range is tied to known uncertainty.
Use the Skill above to help me with this task: Reconcile the gap.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Explain why the methods differ and revise scope or assumptions before presenting a headline range.
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.
04Score reachable segments
Evaluate whether each segment is measurable, substantial, accessible, differentiable, and actionable for the store. Use this when a segment can be reached with a plausible offer, channel, and budget.
Show prompt and variablesHide prompt and variables
Score reachable segments
Evaluate whether each segment is measurable, substantial, accessible, differentiable, and actionable for the store. Use this when a segment can be reached with a plausible offer, channel, and budget.
Use the Skill above to help me with this task: Score reachable segments.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Evaluate whether each segment is measurable, substantial, accessible, differentiable, and actionable for the store.
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 current return policy, not the example rule
The instruction includes example eligibility and resolution paths. Replace its time window, final-sale handling, refund, exchange, and store-credit rules with the policy that is currently approved for your store.
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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