{"uid":"cap_zm4K2uQPbbWlKqjLszgAE","slug":"brand-mention-inventories-record-quality-audit-string-normalisation-3767bc2f","name":"Brand Mention Inventories — Record Quality Audit + String Normalisation + IQR Outlier Flags","description":"Brand mention inventories — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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-18-203?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_Weg5mMB7kqmDbgFVc7bno","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 record quality, normalises strings, and flags IQR-based outliers for brand mention inventory data (mention_count, source_count) across up to 200 rows, returning 3 independent analytical reports.","exampleAgentPrompt":"Can you audit the quality of my brand mention inventory, normalise the company group strings, and flag any IQR outliers in the mention_count field? Here are up to 200 rows with row_id, company_group, date, mention_count, and source_count.","exampleUseCases":[{"title":"Pre-analysis data quality check","prompt":"I have a dataset of brand mentions across different company groups for the past quarter — can you run a record quality audit, normalise the company_group strings, and flag any outliers in mention_count using IQR? I have about 150 rows with row_id, company_group, date, mention_count, and source_count."},{"title":"Outlier detection in source counts","prompt":"Before I report on brand coverage, can you check my brand mention inventory for IQR outliers specifically in the source_count field, normalise any inconsistent company names, and give me a full quality audit? I'll supply around 80 rows."},{"title":"Marketing data cleaning pipeline","prompt":"I'm cleaning up our brand mention tracking data before importing it — can you audit record quality, normalise the company_group strings, and flag IQR-based outliers in mention_count for the 200 rows I have?"}],"resultDescription":"Returns 3 independent analytical reports: (1) a record quality audit identifying missing, malformed, or invalid values; (2) a string normalisation report for company_group and other string fields; and (3) IQR-based outlier flags for the selected numeric field (mention_count or source_count). All analysis is performed locally with no external lookups.","failureModes":["Input exceeds 200 rows — request rejected","Missing required fields (row_id, company_group, date, mention_count, source_count) — validation error","Invalid date format (must be YYYY-MM-DD) — schema validation failure","Numeric values outside allowed range (-1e12 to 1e12) — schema rejection","Empty rows array — minimum 1 item required","Invalid field enum value — must be mention_count or source_count"],"whenToPreferThis":"Choose this endpoint when you need three combined analytical operations — record quality audit, string normalisation, and IQR outlier detection — on brand mention inventory data in a single call. It is ideal for pre-analysis data cleaning pipelines where mention_count and source_count are the key metrics and you want consistent, reproducible local analytics without any external data lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:48:53.202Z","isFirstParty":false,"canonicalSlug":"brand-mention-inventories-record-quality-audit-string-normalisation-3767bc2f"}