{"uid":"cap_juw-XW6MgJh6evN2FtvQU","slug":"feature-availability-daily-sum-series-iqr-outlier-flags-776ed71f","name":"Feature Availability — Daily Sum Series + IQR Outlier Flags","description":"Feature availability — Daily sum series + IQR outlier flags: Daily sum series + IQR outlier flags for supplied feature availability records (populated_features, expected_features). 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-10-140?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"}}},"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_k_3ZqkHLbUNDFk34xuKSD","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 daily sum time series and flags IQR-based outliers for feature availability data (populated_features and/or expected_features) across up to 200 supplied rows.","exampleAgentPrompt":"Analyze my feature availability records — I have up to 50 rows with dates, dataset groups, populated_features, and expected_features — and give me the daily sum series plus IQR outlier flags for the populated_features field.","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I have daily feature availability logs for two dataset groups over the past month — can you compute the daily sum series and flag any outlier dates using IQR for the populated_features column? Here are the rows with row_id, dataset_group, date, populated_features, and expected_features."},{"title":"Expected vs populated feature gap detection","prompt":"Run an IQR outlier analysis on the expected_features field across my 80 feature availability records, grouped by date, so I can see which days had unusually high or low expected feature counts and get the full daily sum series."},{"title":"Multi-group feature availability trend check","prompt":"I've got feature availability data for groups 'alpha' and 'beta' across 120 rows — please compute the daily sum series and IQR outlier flags for populated_features so I can spot which days had abnormal feature population counts."}],"resultDescription":"Returns two independent analytical reports for the supplied data: (1) a daily sum series aggregating the selected field (populated_features or expected_features) by date, and (2) IQR-based outlier flags indicating which dates fall outside the interquartile range. Results cover up to 200 input rows and are computed entirely from the supplied data with no external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, date, populated_features, expected_features) — validation error","Invalid date format (must match YYYY-MM-DD) — pattern validation failure","Invalid field enum value (must be 'populated_features' or 'expected_features') — error","All null values for the selected field — IQR computation may fail or return degenerate results","Fewer than 1 row supplied — minItems constraint violation"],"whenToPreferThis":"Choose this endpoint when you need lightweight, low-cost ($0.01 USDC) local analytical computation over feature availability time series data — specifically when you want both a daily aggregated sum and IQR-based anomaly flags in a single call without requiring external data sources, live predictions, or professional assessment. Best suited for data quality monitoring workflows involving up to 200 records of populated vs expected feature counts grouped by date and dataset group.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:42.207Z","isFirstParty":false,"canonicalSlug":"feature-availability-daily-sum-series-iqr-outlier-flags-776ed71f"}