Skill market 10 skills

When an ASIN goes to the dog page, core keywords stop surfacing, a restricted-product review lands, or the age field does not match, run four skills in order — triage → scan the copy → check the fields → fix the title → write the case — and get a triage table, a hit table, a field checklist and English

Run your listing text and the compliance ledger you filled in yourself against the 19 GPSR / PPWR / EPR / WEEE / battery law / claims clauses one by one, and grade each one "must fix / should fix / needs a human"

Run four checks before you list — high-risk words in your copy, risk baked into the category, the documents your supplier is missing, and whether you can use the images — and get an action list sorted P1/P2/P3. If a complaint, a law firm letter, or a court filing actually arrives, it only triages the type

Turn one product table (an Amazon category flat file or a Shopify product export) into a one-page, tickable launch checklist: every sensitive-word hit, missing category and age fields, image rules, and the declarations your category and market require but you never filed

Two periods of operating data plus this period's action list go in; five things come out: metric-change attribution with hypotheses still to be validated, threshold alerts with a troubleshooting order, a one-page three-part review, three experiments with pass / fail / negative decision lines, and reusable lessons.

Reorder a product page into a publishable draft along first screen → evidence → specs → objections → action, then trace every sentence back to a source record and split the images into a shot list by narrative job.

Run one returns report through three outputs: six root-cause classes grouped by what you can actually fix (size / not as described / damage / expectation gap / logistics / other), the 8 costs behind every single return with an addition formula you can do by hand, and field-level edits telling you which part of the listing to change

Sourcing data tells you how well it sells; only reviews tell you why people buy and why they send it back.

Split Feedback / product reviews / A-to-Z claims into removable / malicious / genuine / not removable, write English removal requests and bilingual reply drafts you can submit as-is, then work out whether this one case will push ODR over the line and how many

Actually crawls PDP / robots.txt / sitemap; six findings anyone can re-check