{"uid":"cap_SREFQpfkFvXB8LNPRJhcW","slug":"feature-availability-analytics-record-quality-audit-string-bd76a2ca","name":"Feature Availability Analytics — Record Quality Audit, String Normalisation & Date-by-Category Pivot","description":"Feature availability — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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-10-206?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_2RzT3LMjVdBQ-YKT6j3lA","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 offline analytical reports (record quality audit, string normalisation, and date-by-category pivot) on up to 200 feature-availability rows containing populated_features and expected_features counts.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and date-by-category pivot on my feature availability data? I have rows for group-a and group-b from 2026-10-01 to 2026-10-04 with populated_features and expected_features counts — analyse the populated_features field.","exampleUseCases":[{"title":"ML pipeline feature coverage audit","prompt":"I need a quality audit on my ML pipeline's feature availability records — can you check for data quality issues and pivot the populated_features counts by date and dataset group? Here are up to 200 rows with row IDs, dataset groups, dates, and populated vs expected feature counts."},{"title":"Data team string consistency check","prompt":"Our data team wants to validate string normalisation and check record quality across our feature availability logs for the past week — can you run all three reports on these 150 rows covering groups alpha, beta, and gamma with their expected and populated feature numbers?"},{"title":"Feature store completeness review","prompt":"Can you analyse our feature store completeness data? I want a record quality audit and a date-by-category pivot on the expected_features field for these dataset groups so we can see where features are missing over time."}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit flagging data issues in the supplied rows, (2) a string normalisation report on text fields such as dataset_group and row_id, and (3) a date-by-category pivot summarising the selected numeric field (populated_features or expected_features) grouped by date and dataset_group. All analysis is performed locally on the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, populated_features, expected_features) causes rejection","Date values not matching YYYY-MM-DD pattern are rejected","Numeric values outside ±1,000,000,000,000 bounds are rejected","Empty rows array is rejected (minItems: 1)","Invalid field enum value (anything other than populated_features or expected_features) causes an error"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical reports on feature availability data in a single $0.01 call — specifically the combination of record quality auditing, string normalisation, and date-by-category pivoting on datasets up to 200 rows. Prefer it over general-purpose analytics tools when your data involves populated_features and expected_features counts grouped by dataset_group and date, and you need a cost-effective offline analysis with no external API dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:21.274Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-record-quality-audit-string-bd76a2ca"}