{"uid":"cap_g5u37U8c8w14PGlWc2vvm","slug":"feature-availability-analytics-record-quality-audit-date-by-category-a8798788","name":"Feature Availability Analytics — Record Quality Audit + Date by Category Pivot","description":"Feature availability — Record quality audit + Date by category pivot: Record quality audit + Date by category pivot 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-10-017?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_lYJjPWNu6eStczPExFDdN","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":"Runs two local analytical reports on feature availability data: a record quality audit and a date-by-category pivot table, processing up to 200 rows per call.","exampleAgentPrompt":"Can you run a record quality audit and date-by-category pivot on my feature availability data? I have rows with populated_features and expected_features across groups like 'group-a' and 'group-b' dated from 2026-10-01 to 2026-10-04 — I want to see completeness issues and a breakdown by date and group.","exampleUseCases":[{"title":"ML dataset feature completeness check","prompt":"I've got a batch of feature availability records for my ML pipeline — can you audit their quality and show me a pivot by date and dataset group so I can see where populated_features falls short of expected_features?"},{"title":"Data pipeline quality gate before training","prompt":"Before I kick off model training, I need a quality audit and date-by-category pivot on these 150 feature availability rows across groups 'finance' and 'retail' — flag any rows where something looks off."},{"title":"Weekly feature coverage report","prompt":"Generate a record quality audit and date pivot for my feature availability data for the week of 2026-10-01 through 2026-10-07 across all my dataset groups, so I can track which categories are consistently under-populated."}],"resultDescription":"Two independent analytical reports: (1) a record quality audit flagging completeness and consistency issues at the row level based on populated_features vs expected_features, and (2) a date-by-category pivot table aggregating feature counts across dataset_group and date dimensions. Both reports are computed locally from supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, populated_features, expected_features) cause row-level rejection","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Null values in both populated_features and expected_features may yield incomplete audit results","Empty rows array returns an error due to minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you need dual analytical reports (quality audit + pivot) on feature availability data in a single low-cost call ($0.01 USDC) without needing live data lookups, predictions, or external integrations. Ideal for data engineers and ML teams who want fast, local computation on batches of up to 200 feature availability records.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:39.885Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-record-quality-audit-date-by-category-a8798788"}