{"uid":"cap_hZ7d-yTGn08xM8wrEsp0k","slug":"competitor-supplied-prices-adjacent-row-changes-explicit-missing-8cfe4cb6","name":"Competitor Supplied Prices — Adjacent Row Changes + Explicit Missing-Value Fill","description":"Competitor supplied prices — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill 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-07-162?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},"field":{"enum":["list_price","package_quantity"],"type":"string","default":"list_price"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_Z2aY6Wqnf1Gm-9jGcy3Sb","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":"Analyzes competitor supplied price records to detect adjacent row changes and fill explicit missing values in list_price or package_quantity fields, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you analyze these competitor price records — up to 200 rows with row_id, company_group, date, list_price, and package_quantity — to detect adjacent row changes and fill any missing list_price values using median imputation? I want both reports back.","exampleUseCases":[{"title":"Detect competitor price shifts over time","prompt":"I have 150 rows of competitor pricing data with row IDs, company groups, dates, and list prices — some list_price values are null. Can you find all adjacent row changes in list_price and fill the missing values using the median method?"},{"title":"Audit package quantity transitions by group","prompt":"Run an adjacent row change analysis and missing-value fill on my competitor dataset for the package_quantity field — use zero-fill for any nulls. I'll send up to 80 rows with row_id, company_group, date, list_price, and package_quantity."},{"title":"Clean competitor pricing data before reporting","prompt":"I need to prep this competitor price list before our weekly report — can you check for row-to-row changes in list_price across company groups and impute missing list_price values using the mean? Here are 200 rows of supplied records."}],"resultDescription":"Two analytical reports: (1) an adjacent row change report highlighting where list_price or package_quantity values differ between consecutive rows, and (2) an explicit missing-value fill report showing which null values were replaced and with what imputed values (zero, mean, or median), based entirely on the supplied input data with no external lookups.","failureModes":["Exceeding 200 rows causes a validation error","Missing required fields (row_id, company_group, date) result in schema rejection","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Field values outside ±1,000,000,000,000 are rejected","Empty rows array returns an error","Invalid method or field enum values are rejected"],"whenToPreferThis":"Use this endpoint when you have locally supplied competitor pricing data (up to 200 rows) and need both row-to-row change detection and missing-value imputation in a single low-cost call. It is ideal for offline data cleaning and audit workflows where no live lookups or external predictions are needed. Choose this over generic data pipelines when your data fits the specific schema (row_id, company_group, date, list_price, package_quantity) and you want two bundled analytical reports for $0.01.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:05.169Z","isFirstParty":false,"canonicalSlug":"competitor-supplied-prices-adjacent-row-changes-explicit-missing-8cfe4cb6"}