{"uid":"cap_Hx_MTlrIgiDkHte_3T7t_","slug":"brand-mention-inventories-numeric-distribution-iqr-outlier-flags-40439440","name":"Brand Mention Inventories — Numeric Distribution + IQR Outlier Flags","description":"Brand mention inventories — Numeric distribution + IQR outlier flags: Numeric distribution + IQR outlier flags for supplied brand mention inventories records (mention_count, source_count). 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-18-080?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_hR1D0uI094fdqPaC0oGfi","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":"Computes numeric distribution statistics and IQR-based outlier flags for brand mention inventory data (mention_count and source_count fields) across up to 200 supplied rows.","exampleAgentPrompt":"Can you run a numeric distribution and IQR outlier analysis on my brand mention inventory data? I have 50 rows with row_id, company_group, date, mention_count, and source_count — please analyze the mention_count field and flag any outliers.","exampleUseCases":[{"title":"Detect spikes in brand mentions","prompt":"I have weekly brand mention data for 3 company groups across 120 rows — can you compute the numeric distribution of mention_count and flag any rows that are IQR outliers so I know which weeks had abnormal activity?"},{"title":"Audit source count anomalies","prompt":"Here are 80 rows of brand monitoring records with company_group, date, and source_count. Can you run distribution stats and IQR outlier detection on the source_count field to find any suspicious entries?"},{"title":"Compare mention spread across brand groups","prompt":"I want to understand the statistical spread of mention_count values across my brand groups — I have 150 rows of inventory data. Can you generate the numeric distribution report and highlight any outliers using IQR?"}],"resultDescription":"Returns two analytical reports for up to 200 rows: (1) a numeric distribution summary (e.g. min, max, mean, median, quartiles) for the selected field (mention_count or source_count), and (2) IQR-based outlier flags identifying which rows fall outside the expected range. No external data is fetched; all analysis is performed on the supplied rows.","failureModes":["Input exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, company_group, date, mention_count, source_count) cause validation errors","Invalid date format (must match YYYY-MM-DD pattern) triggers schema rejection","Null values in the selected numeric field may affect distribution calculations","Field enum value not matching 'mention_count' or 'source_count' causes parameter error","Empty rows array (minItems: 1 not met) returns an error"],"whenToPreferThis":"Choose this endpoint when you have a pre-collected dataset of brand mention inventory records (up to 200 rows) and need fast, local statistical analysis — specifically numeric distribution summaries and IQR-based outlier detection — without requiring live data lookup, external APIs, or ML predictions. Ideal for data quality audits, anomaly detection in brand tracking pipelines, or exploratory analysis of mention and source count distributions across company groups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T18:57:12.532Z","isFirstParty":false,"canonicalSlug":"brand-mention-inventories-numeric-distribution-iqr-outlier-flags-40439440"}