{"uid":"cap_XB9VzlOluE7AqVnbvw0xW","slug":"brand-mention-inventories-record-quality-audit-string-normalisation-89e12948","name":"Brand Mention Inventories — Record Quality Audit + String Normalisation + Adjacent Row Changes","description":"Brand mention inventories — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied brand mention inventories records (mention_count, source_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-company-18-200?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","mention_count","source_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"mention_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_count":1,"company_group":"group-a","mention_count":10},{"date":"2026-10-02","row_id":"row-2","source_count":2,"company_group":"group-b","mention_count":20},{"date":"2026-10-03","row_id":"row-3","source_count":3,"company_group":"group-a","mention_count":15},{"date":"2026-10-04","row_id":"row-4","source_count":4,"company_group":"group-b","mention_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["mention_count","source_count"],"type":"string","default":"mention_count"}}},"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_rKOhbLqzA6uED0MYAaBMt","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":"Runs three local analytical reports on brand mention inventory rows: record quality audit, string normalisation, and adjacent row change detection for mention_count or source_count fields.","exampleAgentPrompt":"Can you run all three local quality reports — record quality audit, string normalisation, and adjacent row changes — on my brand mention inventory data for mention_count? I have up to 200 rows with company_group, date, row_id, mention_count, and source_count fields.","exampleUseCases":[{"title":"Brand mention data quality check","prompt":"I have a CSV of brand mention records with columns row_id, company_group, date, mention_count, and source_count — can you run the record quality audit, string normalisation, and adjacent row changes reports on all 150 rows using mention_count as the target field?"},{"title":"Detecting source count anomalies between rows","prompt":"Please analyse my brand mention inventory dataset of 80 rows and run all three reports focusing on source_count — I want to see the quality audit, any string normalisation issues in company_group, and where source counts jump between adjacent rows."},{"title":"Cleaning company group labels in mention data","prompt":"My brand mention inventory has inconsistent company_group strings across 60 rows — can you run the string normalisation report along with the quality audit and adjacent row changes for mention_count so I can see all the issues at once?"}],"resultDescription":"Three independent analytical reports for the submitted rows: (1) a record quality audit flagging data issues per row, (2) a string normalisation report highlighting inconsistencies in text fields like company_group, and (3) an adjacent row changes report showing value shifts between consecutive rows for the selected numeric field (mention_count or source_count). All computed locally with no external lookups.","failureModes":["Missing required fields (row_id, company_group, date, mention_count, source_count) cause validation errors","Submitting more than 200 rows exceeds the row limit","Invalid date format (not YYYY-MM-DD) triggers schema rejection","Numeric values outside the allowed range cause field errors","Invalid field enum value (not mention_count or source_count) rejected","Payment failure via x402 protocol returns 402 error"],"whenToPreferThis":"Choose this endpoint when you have brand mention inventory data (with mention_count and source_count) and need three simultaneous local analytical reports — quality audit, string normalisation, and adjacent row change detection — without any external data lookups, predictions, or live enrichment. Ideal for data cleaning and QA pipelines on batches up to 200 rows at very low cost ($0.01 for all three reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T18:57:16.919Z","isFirstParty":false,"canonicalSlug":"brand-mention-inventories-record-quality-audit-string-normalisation-89e12948"}