{"uid":"cap_yvSNFhEY3WPoB9M2GJZ4Y","slug":"feature-availability-record-quality-audit-and-adjacent-row-changes-56365bac","name":"Feature Availability Record Quality Audit and Adjacent Row Changes","description":"Feature availability — Record quality audit + Adjacent row changes: Record quality audit + Adjacent row changes 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-10-011?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_7wUnQ8euOLURipB5Gxq9s","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 adjacent row change detection, for up to 200 rows.","exampleAgentPrompt":"Can you run a record quality audit and adjacent row changes report on my feature availability data? I have 4 rows across group-a and group-b dated 2026-10-01 through 2026-10-04, with populated_features values of 10, 20, 15, 30 and expected_features of 1, 2, 3, 4 — analyze the populated_features field.","exampleUseCases":[{"title":"ML pipeline feature completeness check","prompt":"I have a batch of 50 feature availability records from my ML pipeline across three dataset groups — can you audit the record quality and flag any adjacent row changes in the populated_features column for dates from 2026-09-01 through 2026-09-20?"},{"title":"Dataset group drift detection","prompt":"Run an adjacent row changes and quality audit on these feature availability rows for group-b — I want to see where expected_features jumped or dropped unexpectedly between consecutive dates in October 2026."},{"title":"Data ingestion validation for analytics team","prompt":"We just ingested a new batch of feature records for group-alpha and I need both a quality audit and adjacent row change report on the populated_features field before we push to production — can you process these 150 rows?"}],"resultDescription":"Returns two independent analytical reports for the submitted feature availability rows: (1) a record quality audit flagging completeness and consistency issues across populated_features and expected_features, and (2) an adjacent row changes report showing deltas or anomalies between consecutive rows. Both reports are computed locally with no external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, populated_features, expected_features) cause row-level rejection","Malformed date strings not matching YYYY-MM-DD pattern cause input rejection","Null values in both populated_features and expected_features may limit audit depth","Invalid field selector enum value causes request failure"],"whenToPreferThis":"Choose this endpoint when you need two simultaneous analytical passes — quality audit plus sequential change detection — on a batch of feature availability records without any external data lookups or live predictions. It is ideal for validating ML dataset completeness and spotting row-level drift in a single low-cost call covering up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:35.534Z","isFirstParty":false,"canonicalSlug":"feature-availability-record-quality-audit-and-adjacent-row-changes-56365bac"}