{"uid":"cap_YHOUOe15rryA0Xvx-jSpy","slug":"feature-availability-record-quality-audit-iqr-outlier-flags-4df03e77","name":"Feature Availability Record Quality Audit + IQR Outlier Flags","description":"Feature availability — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags 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-10-014?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_MpgF6ggtqu-FvvmkABe6Y","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 a record quality audit and IQR-based outlier detection on up to 200 feature availability records (populated_features, expected_features), returning two independent analytical reports.","exampleAgentPrompt":"Audit the quality of these feature availability records and flag any IQR outliers in the populated_features column — I have about 50 rows spanning October 2026 across two dataset groups, group-a and group-b.","exampleUseCases":[{"title":"ML pipeline feature completeness audit","prompt":"I've got 120 rows of feature availability data from my ML pipeline — each row has a date, a dataset group, and counts of populated vs expected features. Can you run a record quality audit on them and flag any outliers in populated_features using IQR?"},{"title":"Data health check before model training","prompt":"Before we kick off model training, can you audit these feature availability records for quality issues and identify any rows where expected_features looks like an outlier? I have about 80 rows across three dataset groups from last quarter."},{"title":"Anomaly detection in feature tracking logs","prompt":"We track feature population rates daily across several dataset groups — can you analyze these 150 rows of feature availability logs, run the record quality audit, and flag any IQR outliers in the expected_features field?"}],"resultDescription":"Returns two independent reports: (1) a record quality audit identifying missing, malformed, or invalid rows among the supplied feature availability records, and (2) an IQR-based outlier detection report flagging rows where the selected field (populated_features or expected_features) falls outside the interquartile range bounds. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, date, populated_features, expected_features) cause row-level validation errors","Date values not matching YYYY-MM-DD pattern are rejected","Null numeric values may affect IQR computation if too prevalent","Payment of 0.01 USDC not completed results in 402 response"],"whenToPreferThis":"Choose this endpoint when you need both a data quality audit and IQR outlier detection on feature availability records in a single low-cost call ($0.01 USDC). Ideal for ML/data engineering workflows where you want to validate feature completeness data before model training or pipeline steps, without requiring any external data sources or live predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:40.206Z","isFirstParty":false,"canonicalSlug":"feature-availability-record-quality-audit-iqr-outlier-flags-4df03e77"}