{"uid":"cap_re6Bhn7hXQ9BjpSzww5_E","slug":"feature-availability-record-quality-audit-exact-row-deduplication-9b428b9e","name":"Feature Availability — Record Quality Audit + Exact Row Deduplication","description":"Feature availability — Record quality audit + Exact row deduplication: Record quality audit + Exact row deduplication 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-10-002?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_hVGK34WVnFo4FHlFFNUJ0","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 independent analytical reports on supplied feature availability records: a record quality audit and exact row deduplication, for up to 200 rows per call.","exampleAgentPrompt":"Can you run a quality audit and exact row deduplication check on these feature availability records — I have rows for groups 'model-v1' and 'model-v2' across dates in October 2026, each with populated_features and expected_features counts, and I want both reports back for up to 50 rows?","exampleUseCases":[{"title":"ML pipeline feature coverage audit","prompt":"I have a batch of feature availability records from my ML pipeline — rows tagged by dataset group 'training-set-a' and 'training-set-b' with dates and populated vs expected feature counts. Can you run a quality audit on them to flag any coverage gaps or data issues?"},{"title":"Deduplication check before data ingestion","prompt":"Before I ingest these 120 feature availability rows into our data warehouse, can you run an exact row deduplication check to make sure there are no duplicate entries across the row_id, date, and dataset_group fields?"},{"title":"Feature availability health report by group","prompt":"I want to understand the data quality for our feature availability tracking — can you audit these records across dataset groups 'prod-group-1' and 'prod-group-2' for October 2026 and give me both a quality report and a duplicate row report?"}],"resultDescription":"Returns two independent analytical reports: (1) a record quality audit covering completeness, consistency, and anomalies in populated_features vs expected_features across rows; and (2) an exact row deduplication report identifying any duplicate rows across the supplied dataset. Both reports are generated using local analytical engines with no external lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, populated_features, expected_features) — validation error","Date not matching YYYY-MM-DD format — pattern validation failure","Empty rows array — minItems constraint violation","populated_features or expected_features out of allowed numeric range — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need both a data quality audit and exact deduplication on feature availability records in a single low-cost call. It is well-suited for ML/data engineering pipelines where you want to validate populated vs expected feature counts and catch duplicate rows before downstream ingestion, without any external API calls or live data lookups. Ideal for batches up to 200 rows where local, self-contained analytical reports are sufficient.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:32.516Z","isFirstParty":false,"canonicalSlug":"feature-availability-record-quality-audit-exact-row-deduplication-9b428b9e"}