{"uid":"cap_9jRf9LuSMCoEQ7Mn3ak0p","slug":"feature-availability-base-100-series-iqr-outlier-flags-analytics-f6bb98e6","name":"Feature Availability Base-100 Series + IQR Outlier Flags Analytics","description":"Feature availability — Base-100 series + IQR outlier flags: Base-100 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-10-164?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"},"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_TGO3tsJtVPrgMrvg_jCZ-","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 Base-100 index series and IQR-based outlier flags for feature availability data (populated_features, expected_features) across up to 200 rows, delivering two independent analytical reports.","exampleAgentPrompt":"I have up to 200 rows of feature availability data with populated_features and expected_features per date and dataset group — can you run a Base-100 series normalization (starting at index 0) on populated_features and flag any IQR outliers across the records?","exampleUseCases":[{"title":"Daily feature completeness audit","prompt":"I've got daily feature availability records for two dataset groups over the past quarter — can you index the populated_features values to a Base-100 series starting at row 0 and flag any days where the counts look like IQR outliers?"},{"title":"Detect anomalous expected feature counts","prompt":"Run IQR outlier detection and a Base-100 series on expected_features across my 150 dataset rows grouped by project — I want to see which records are statistically anomalous compared to the rest."},{"title":"Cross-group feature population trend analysis","prompt":"I have feature availability records across two dataset groups spanning several months. Can you normalize the populated_features to a Base-100 index from the first row and highlight any outliers using IQR so I can see which groups are underperforming?"}],"resultDescription":"Two independent analytical reports: (1) a Base-100 series mapping each row's selected field value relative to the chosen base index row, and (2) IQR-based outlier flags indicating which rows fall outside the interquartile range for the selected field — all computed locally without external lookups.","failureModes":["Input array exceeds 200 rows — rejected with validation error","Missing required fields (row_id, date, populated_features, expected_features) cause row-level rejection","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","base_index references a row index out of bounds for the supplied array","Null values in the selected field may affect IQR computation or Base-100 indexing","All values identical produces degenerate IQR result with no outliers flagged"],"whenToPreferThis":"Choose this endpoint when you need both Base-100 time-series normalization and IQR outlier detection on feature availability data in a single call at minimal cost ($0.01 USDC for two reports). Ideal for data quality pipelines auditing feature completeness across dataset groups over time, especially when you want statistical anomaly detection without external model inference or live data lookup. Prefer over single-report endpoints when both normalization and outlier flagging are needed simultaneously.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:15:40.855Z","isFirstParty":false,"canonicalSlug":"feature-availability-base-100-series-iqr-outlier-flags-analytics-f6bb98e6"}