{"uid":"cap_kfqzONBmURShE04v8oaps","slug":"null-coverage-analytics-base-100-series-iqr-outlier-flags-9968a6db","name":"Null Coverage Analytics — Base-100 Series + IQR Outlier Flags","description":"Null coverage — Base-100 series + IQR outlier flags: Base-100 series + IQR outlier flags for supplied null coverage records (missing_values, total_values). 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-analytics-03-164?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","missing_values","total_values"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"missing_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_values":1,"dataset_group":"group-a","missing_values":10},{"date":"2026-10-02","row_id":"row-2","total_values":2,"dataset_group":"group-b","missing_values":20},{"date":"2026-10-03","row_id":"row-3","total_values":3,"dataset_group":"group-a","missing_values":15},{"date":"2026-10-04","row_id":"row-4","total_values":4,"dataset_group":"group-b","missing_values":30}],"maxItems":200,"minItems":1},"field":{"enum":["missing_values","total_values"],"type":"string","default":"missing_values"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_BamuQRjYu_65KnadM4wnU","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 a base-100 indexed time series and IQR-based outlier flags for user-supplied null coverage data (missing_values, total_values) across up to 200 rows, delivering two analytical reports per call.","exampleAgentPrompt":"I have a set of null coverage records — up to 50 rows with date, dataset_group, missing_values, and total_values — can you compute a base-100 indexed series starting from index 0 on the missing_values field, and flag any IQR outliers in the data?","exampleUseCases":[{"title":"Data pipeline completeness monitoring","prompt":"I've got daily null coverage logs from my ETL pipeline — about 90 rows, each with a date, group name, missing_values, and total_values. Can you index the missing_values field to a base-100 series starting at row 0 and flag any outlier rows using IQR?"},{"title":"Cross-group missing data comparison","prompt":"I have null coverage records across two dataset groups — 'sales' and 'inventory' — for the past quarter, around 60 rows. Can you normalize the total_values to a base-100 series from the first row and tell me which rows are IQR outliers?"},{"title":"Audit report on data quality anomalies","prompt":"I need an audit of our data quality: here are 150 rows of null coverage data with dates, group labels, missing_values, and total_values. Please generate a base-100 index on missing_values starting at position 0 and highlight any rows that fall outside the IQR bounds."}],"resultDescription":"Returns two analytical reports: (1) a base-100 indexed time series for the selected field (missing_values or total_values) anchored at the specified base_index row, and (2) IQR-based outlier flags marking which rows fall outside the interquartile range, all computed locally without external data lookups or predictions.","failureModes":["Missing required fields (row_id, date, missing_values, total_values) cause validation errors","Date strings not matching YYYY-MM-DD pattern are rejected","Array exceeding 200 rows is rejected with a maxItems error","base_index out of range (0–199) causes a validation error","null values in missing_values or total_values may affect IQR computation depending on implementation","Payment failure via x402 protocol returns 402 before processing"],"whenToPreferThis":"Choose this endpoint when you have pre-collected null coverage or data completeness records and need both a normalized trend index and statistical outlier detection in a single low-cost call. It is ideal for data quality audits, ETL monitoring, and completeness reporting workflows where all data is already available locally and no live lookup, prediction, or external enrichment is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:45:01.879Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-base-100-series-iqr-outlier-flags-9968a6db"}