{"uid":"cap_fV7oKmcOyJLMvVIZ4UpjD","slug":"competitor-supplied-prices-record-quality-audit-string-normalisation-09a4c415","name":"Competitor Supplied Prices — Record Quality Audit + String Normalisation","description":"Competitor supplied prices — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied competitor supplied prices records (list_price, package_quantity). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-company-07-001?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","list_price","package_quantity"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"list_price":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"package_quantity":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","list_price":10,"company_group":"group-a","package_quantity":1},{"date":"2026-10-02","row_id":"row-2","list_price":20,"company_group":"group-b","package_quantity":2},{"date":"2026-10-03","row_id":"row-3","list_price":15,"company_group":"group-a","package_quantity":3},{"date":"2026-10-04","row_id":"row-4","list_price":30,"company_group":"group-b","package_quantity":4}],"maxItems":200,"minItems":1}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_bHaTacaC6tEnIYIr6P60X","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Audits and string-normalises up to 200 rows of competitor-supplied price records (list_price, package_quantity), producing two independent analytical reports using local engines.","exampleAgentPrompt":"Can you run a record quality audit and string normalisation on these competitor price rows? I have up to 50 rows each with a row_id, company_group, date, list_price, and package_quantity — I want two reports flagging any data quality issues and normalising the text fields.","exampleUseCases":[{"title":"Pre-analysis competitor price data cleanup","prompt":"I've scraped competitor pricing from three suppliers and want to clean it up before loading it into my model — can you audit these 120 rows for record quality issues and normalise the company_group and other string fields? Each row has a row_id, company_group, date, list_price, and package_quantity."},{"title":"Validating manually entered price records","prompt":"My team manually entered 80 competitor price records with fields for row_id, company_group, date, list_price, and package_quantity — can you run a quality audit to catch typos, malformed strings, and suspicious values, and also give me a string normalisation report?"},{"title":"Detecting anomalies before competitor benchmarking","prompt":"Before I benchmark our prices against competitors, I need to check the 150 rows we received from our data vendor — please run a record quality audit and string normalisation on them, covering list_price and package_quantity, so I can flag anything weird before the analysis."}],"resultDescription":"Two independent reports: (1) a record quality audit identifying issues such as missing values, out-of-range numbers, malformed dates, or duplicate row_ids across the submitted rows; (2) a string normalisation report showing standardised and cleaned text field values (e.g. company_group). Both reports cover up to 200 rows and are generated using local analytical engines with no external lookups.","failureModes":["Fewer than 1 row submitted — request rejected","More than 200 rows submitted — exceeds batch limit","row_id missing or empty — validation error per row","date field does not match YYYY-MM-DD pattern — rejected row","list_price or package_quantity outside allowed numeric range — flagged or rejected","Payment of 0.01 USDC not received — 402 response","Malformed JSON body — 400 error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of competitor-supplied pricing records (with list_price and package_quantity fields) and need both a data quality audit and string normalisation in a single low-cost call. It is ideal for pre-processing pipelines before downstream price analysis or benchmarking, and when you want self-contained local analysis with no external API calls or live data lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:44:49.087Z","isFirstParty":false,"canonicalSlug":"competitor-supplied-prices-record-quality-audit-string-normalisation-09a4c415"}