# Listing Hygiene Check > Check listings and reviews for the usual abuse - Skill: `mrmps/listing-hygiene-check` (Agent Skill) - Install (CLI): `npx skillmds@latest add mrmps/listing-hygiene-check` - Raw SKILL.md: https://api.skillmd.com/api/skills/mrmps/listing-hygiene-check/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: mrmps (https://skillmd.com/u/mrmps) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/mrmps/listing-hygiene-check --- # Check listings and reviews for the usual abuse `classifier.dev` tags text against labels you write and returns a score per label. No key. Three passes over a catalogue export give a category, a set of policy signals and a review-abuse read, each with a number. It removes nothing and messages nobody. It fills a queue. ## When not to use this - Enforcement. Nothing here suspends a seller, delists an item or answers a trademark claim. Those need a person, usually with counsel. - Anything turning on the image, the invoice, the seller's history or a brand's authorised-seller list. This reads listing text only. - Prohibited lists (knives, supplements, safety marks) differ by market. Write the labels per market, with whoever owns policy. ## Pass 1 — category, with an escape hatch ``` "labels": ["consumer electronics and accessories", "kitchen and home", "health and supplements", "knives and weapons", "none of these"] ``` Single label. Miscategorisation is an old listing trick, and a category that disagrees with a signal is a strong review candidate. Keep `none of these`: without it every listing lands in a category whatever it sells. ## Pass 2 — policy signals, multi-label A listing can break two rules at once, so ask for every label that applies. ``` curl -s https://classifier.dev/v1/classify \ -H 'content-type: application/json' \ -d '{ "labels": ["counterfeit or replica of a known brand", "restricted or prohibited item: weapons, drugs, live animals", "unapproved health or medical claim", "condition misstated: used or refurbished sold as new", "pushes the buyer to contact or pay off the platform", "no policy problem found"], "instructions": "Flag every signal the listing text actually shows. Do not flag a signal that is only implied by the category.", "multi": true, "max_labels": 3, "inputs": ["Aple AirPads Pro 1:1 Original Quality, sealed box, same chip as retail. Message us on WhatsApp for bulk pricing.", "Cast Iron Skillet 26cm, pre-seasoned, oven safe to 260C, made in Portugal, 2.9kg"] }' ``` Real output. `labels` holds everything at or above 0.7, most likely first; `scores` holds all six either way: ``` ["pushes the buyer to contact or pay off the platform", "counterfeit or replica of a known brand"] counterfeit 0.79 restricted 0.01 health claim 0.03 condition 0.21 off platform 0.93 no problem 0.22 ["no policy problem found"] every risk score 0.03 or lower no problem 0.72 ``` Two labels fired on one listing: the misspelled brand and the off-platform contact are separate violations with separate remedies. ## Pass 3 — review abuse, on the reviews Reviews are their own corpus. Classify review text, `max_labels: 2`: ``` "labels": ["paid or incentivised review: free product, refund or discount for the review", "duplicated or templated text", "the seller writing as a customer", "solicits reviews off platform", "ordinary review, positive or negative"] ``` Measured on six real-shaped reviews: "Got this free in exchange for my honest review" scored incentivised 0.98; one offering a discount code for any review fired incentivised 0.98 and off-platform 0.96 together. A negative two-star review scored ordinary, 0.93 — say in `instructions` that abuse is not a bad rating, or the pass becomes a sentiment filter. ## Severity from the score band Per signal, not per listing. Each label carries its own score: - **0.9 and above** — high severity. Queue it at the top and suppress the listing from promoted placements pending a check, which is reversible. - **0.5 to 0.9** — medium. Queue for review. Nothing changes for the seller. - **Under 0.5** — log the score and move on. Nothing is delisted, deleted or messaged by this. A person works the queue. **Clear a listing on the risk scores, not the clean label.** Multi-label scores are independent, and `no policy problem found` has no reason to be high: clean listings came back at 0.72, 0.77 and 0.78, with every risk score at 0.04 or lower. Auto-clear when `max(risk scores) < 0.5`, never on the clean label. ## The queue file One JSON object per line, written by your code, never by the classifier: ``` {"id":"L-101","category":"consumer electronics and accessories","cat_conf":1.0, "signals":[["off platform",0.93],["counterfeit",0.79]],"severity":"high","action":"none yet"} ``` Keep every score, the ones under 0.5 included. When a reviewer overturns a flag you need the number behind it to say whether the label or the threshold was wrong. ## Pitfalls - **`max_labels` truncates and 0.7 is the floor.** Four problems return three at `max_labels: 3`, and a signal at 0.6 is missing from `labels` entirely. The `scores` map has everything; your medium band lives there. - **`labels` can be empty.** A plain chef's knife listing returned `[]`: no risk signal reached 0.7 and neither did `no policy problem found`, at 0.58. Code that reads `labels[0]` breaks here. Read `scores`. - **Multi-label results have no `confidence` field.** Each label has its own score and the bands apply per score; reading `confidence` here is a KeyError. - **Sellers adapt.** Re-read the 0.5 to 0.9 band monthly: evasions appear there as near-ties before they appear as confident hits. - Limits per IP: 3,000 classifications a minute, 20,000 a day. Three passes over 1,000 listings is 3,000; a 429 carries `Retry-After`.