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.
IntermediateAlireza Rezvani
Product Discovery
Product Discovery is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to you are still deciding which…
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What this Skill does
You are still deciding which product, market, or direction deserves investment. Bring Validate whether a new product category is worth adding to your store before… and Map and test assumptions about customer pain points before building features into the same operating path… Start with a small test around “Validate whether a new product category is worth adding to your store before sourcing”, then check whether “Map and test assumptions about customer pain points before building features” fits the way your team actually works.
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
Actual return-logistics information
Provide the approved label process, carrier or drop-off details, and the real refund timing. The source text can format these instructions but cannot look them up.
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 discovery facilitator. De-risk product bets: 1) Define one measurable outcome with baseline and target. 2) Build an Opportunity Solution Tree: Outcome → Opportunities (user evidence, not opinions) → Solutions → Experiments. Require ≥3 distinct opportunities before converging, ≥2 experiments per top opportunity. 3) Map assumptions across desirability, viability, feasibility, usability — score by risk/certainty; test high-risk/low-certainty first. Use assumption_mapper.py for scoring. 4) Validate problems via interviews and behavior analysis; validate solutions via prototypes, concept tests, fake-door experiments. 5) Plan 10-day sprint with daily evidence reviews. End with: proceed, pivot, or stop. Tie every branch to evidence.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 Discovery can help you research demand, competitors, customer needs, and product opportunities. Use this when the input boundary, owner, and one primary measure from source coverage, estimate error, margin viability, and decision confidence 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 Discovery can help you research demand, competitors, customer needs, and product opportunities. Use this when the input boundary, owner, and one primary measure from source coverage, estimate error, margin viability, and decision confidence 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 Discovery can help you research demand, competitors, customer needs, and product opportunities.
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 a precise research question, market and date boundaries, product economics, and rejection criteria 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 a precise research question, market and date boundaries, product economics, and rejection criteria 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 a precise research question, market and date boundaries, product economics, and rejection criteria 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 Discovery to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a market or product decision brief 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 Discovery to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a market or product decision brief 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 Discovery 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 source coverage, estimate error, margin viability, and decision confidence 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 source coverage, estimate error, margin viability, and decision confidence 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
Do not invent labels, links, locations, or timelines
This text can draft return instructions, but it does not verify a carrier label, local drop-off point, parcel status, or refund-processing time. Use only details supplied by your actual return system.
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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