{"uid":"cap_lc3wvCtb5KK67vlsGybhe","slug":"feature-availability-analytics-record-quality-audit-exact-row-34f2d968","name":"Feature Availability Analytics — Record Quality Audit, Exact Row Deduplication & IQR Outlier Flags","description":"Feature availability — Record quality audit + Exact row deduplication + IQR outlier flags: Record quality audit + Exact row deduplication + IQR outlier flags for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-220?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_V2J42t_qOFIaWiCE8Sjoe","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 three local analytical reports on feature availability records: a record quality audit, exact row deduplication, and IQR-based outlier flagging — for up to 200 rows of populated_features / expected_features data.","exampleAgentPrompt":"Can you run a quality audit, deduplication check, and IQR outlier detection on my feature availability data? I have rows with row_id, dataset_group, date, populated_features, and expected_features — flag any bad records, exact duplicates, and statistical outliers in the populated_features column.","exampleUseCases":[{"title":"Pre-pipeline feature data validation","prompt":"Before I push this feature availability dataset into my ML pipeline, can you audit the data quality, remove exact duplicate rows, and flag any IQR outliers in the populated_features column? I have about 150 rows with row_id, dataset_group, date, populated_features, and expected_features."},{"title":"Detecting anomalies in feature completeness tracking","prompt":"I'm tracking how many features are populated versus expected across multiple dataset groups over the past few months. Can you run an outlier analysis using IQR on the expected_features field and also check for any duplicate records or data quality issues?"},{"title":"Auditing feature store records for reporting","prompt":"I need a data integrity report on my feature availability records — specifically a quality audit, exact row deduplication, and IQR outlier flags on the populated_features values. I have around 80 rows across groups like group-a and group-b with dates in 2026."}],"resultDescription":"Three independent analytical reports: (1) a record quality audit highlighting malformed or problematic rows, (2) an exact row deduplication report identifying duplicate records, and (3) IQR-based outlier flags marking statistically anomalous values in the selected numeric field (populated_features or expected_features). All computed locally 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 (non-YYYY-MM-DD) triggers pattern validation failure","Null values in both populated_features and expected_features may reduce outlier detection accuracy","Fewer than 4-5 rows may produce unreliable IQR statistics"],"whenToPreferThis":"Choose this endpoint when you need three combined analytical operations — quality audit, deduplication, and IQR outlier detection — on feature availability records in a single low-cost call. Ideal for validating feature store data before ML pipeline ingestion, or for periodic data health checks on datasets up to 200 rows. Prefer this over separate single-purpose analytics tools when cost efficiency and bundled reporting on populated_features / expected_features data is the priority.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:55.091Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-record-quality-audit-exact-row-34f2d968"}