{"uid":"cap_QwXLea2GunJ4BlqHWVAOX","slug":"feature-availability-record-quality-audit-category-frequencies-6a626f67","name":"Feature Availability — Record Quality Audit + Category Frequencies","description":"Feature availability — Record quality audit + Category frequencies: Record quality audit + Category frequencies 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-10-006?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}}},"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_29kmPNpteGtfP_Jdqief_","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":"Audits record quality and computes category-level frequency distributions for up to 200 feature availability records (populated_features, expected_features) using local analytical engines — no external lookups.","exampleAgentPrompt":"Can you run a record quality audit and category frequency analysis on my feature availability data? I have 4 rows — row-1 through row-4 across group-a and group-b dated 2026-10-01 to 2026-10-04, with populated_features of 10, 20, 15, 30 and expected_features of 1, 2, 3, 4 respectively.","exampleUseCases":[{"title":"ML pipeline feature completeness audit","prompt":"I need to audit the feature completeness for my ML training dataset — can you check these 50 records across dataset groups 'train', 'val', and 'test' and tell me which rows have quality issues and how frequently each group appears?"},{"title":"Daily data quality check for feature store","prompt":"Run a quality audit and category frequency report on today's feature store snapshot — I have 120 records with row IDs, dates around 2025-07-15, split across group-a and group-b, with populated_features and expected_features filled in. Flag anything that looks off."},{"title":"Comparing feature population across business units","prompt":"I have feature availability data for 80 records from three business units — 'finance', 'marketing', and 'ops' — dated last quarter. Can you generate a record quality audit and show me how frequently each unit appears in the dataset?"}],"resultDescription":"Returns two independent analytical reports: (1) a record quality audit highlighting anomalies, nulls, or out-of-range values among the supplied rows, and (2) a category frequency table showing the distribution of records across dataset_group values. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds batch limit","Missing required fields (row_id, date, populated_features, expected_features) — validation error","Date fields not matching YYYY-MM-DD pattern — schema rejection","populated_features or expected_features out of allowed numeric range — validation error","Payment not confirmed via x402 — endpoint returns 402"],"whenToPreferThis":"Choose this endpoint when you need a quick, cost-effective ($0.01 USDC) dual-report analysis of feature availability records — specifically record-level quality auditing combined with dataset_group frequency profiling — on batches up to 200 rows, without requiring any external data sources or live lookups. Ideal for ML/data engineering workflows that need lightweight, local analytical validation.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:38.959Z","isFirstParty":false,"canonicalSlug":"feature-availability-record-quality-audit-category-frequencies-6a626f67"}