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.
IntermediateHarryLabsJ
Search Listing Optimizer
Search Listing Optimizer is an ecommerce AI skill for HarryLabsJ, built for teams working with OpenClaw. Use it to you are investigating why a product, category, or…
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What this Skill does
You are investigating why a product, category, or landing page is missing expected organic traffic. The first decision to test is whether Audit an Amazon listing for A9 keyword visibility and Diagnose a JD listing that gets traffic but no conversions hold up together before… Start with a small test around “Audit an Amazon listing for A9 keyword visibility”, then check whether “Diagnose a JD listing that gets traffic but no conversions” 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.
Creates listing and search-content fields together
The source covers product or listing copy alongside SEO-related fields such as keywords, metadata, or image text when those fields are requested.
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 marketplace search optimization specialist. Audit the user's product listing and produce an optimization brief. Steps: 1) Capture platform (Amazon, Taobao, JD, TikTok Shop, Xiaohongshu, Shopify), product context, and goal (visibility, conversion, new listing, relaunch); 2) Apply keyword-density and search-intent frameworks for that platform's ranking behavior; 3) Check attribute completeness across key fields (title, bullets, backend keywords, specs); 4) Detect conversion traps — pricing mismatches, weak images, thin reviews, vague descriptions; 5) Return a markdown brief with prioritized action items (P0/P1/P2). The analysis is heuristic from listing notes, not live platform data — state assumptions explicitly.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 Search Listing Optimizer can help you create, localize, or check product content and visual assets. Use this when the input boundary, owner, and one primary measure from factual corrections, editing time, approval rate, and conversion quality 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 Search Listing Optimizer can help you create, localize, or check product content and visual assets. Use this when the input boundary, owner, and one primary measure from factual corrections, editing time, approval rate, and conversion quality 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 Search Listing Optimizer can help you create, localize, or check product content and visual assets.
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 verified product facts, source images, brand rules, target channel, and prohibited claims 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 verified product facts, source images, brand rules, target channel, and prohibited claims 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 verified product facts, source images, brand rules, target channel, and prohibited claims 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 Search Listing Optimizer to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a product-content draft 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 Search Listing Optimizer to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a product-content draft 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 Search Listing Optimizer 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 factual corrections, editing time, approval rate, and conversion quality 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 factual corrections, editing time, approval rate, and conversion quality 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 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.
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.
Verify commercial terms before acting
Any price, discount, cost, or margin recommendation is only as current as the values you provide. Recheck live prices, tax, shipping, and margin rules before publishing or sending an offer.
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.
HarryLabsJ
ClawHub skill author focused on search visibility, product listings, and ecommerce content optimization.
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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