{"uid":"cap_E2vy3S8OUMoLRSxElPkAO","slug":"feature-availability-analytics-category-frequencies-adjacent-row-1f76ea82","name":"Feature Availability Analytics: Category Frequencies & Adjacent Row Changes","description":"Feature availability — Category frequencies + Adjacent row changes: Category frequencies + 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-10-104?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_YT3ECkZorwrJ1AUIKHVca","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":"Analyzes feature availability records to produce two reports: category frequency distributions and adjacent row changes, for up to 200 rows of populated/expected feature data.","exampleAgentPrompt":"Analyze these feature availability records for me — I want both the category frequency breakdown by dataset_group and the adjacent row changes for populated_features across my 50 rows.","exampleUseCases":[{"title":"ML dataset feature completeness audit","prompt":"I have 150 rows of ML dataset records with populated_features and expected_features columns across two groups, 'train' and 'test' — can you give me the category frequency distribution by group and show me how populated_features changes between consecutive rows?"},{"title":"Daily feature pipeline health check","prompt":"Here are my daily feature pipeline records from the past 30 days with dates, dataset_group labels, and counts for populated_features and expected_features — run the category frequencies and adjacent row changes report on the populated_features field so I can see where drops occur."},{"title":"Multi-group feature availability comparison","prompt":"I've got feature availability data for 4 dataset groups over the last quarter, up to 200 rows total — analyze the expected_features field to get me the category frequencies per group and flag where values shift significantly between adjacent rows."}],"resultDescription":"Returns two independent analytical reports computed locally: (1) category frequencies showing how often each dataset_group category appears in the supplied records, and (2) adjacent row changes showing the delta between consecutive rows for the selected field (populated_features or expected_features). No external lookups or predictions are made.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, date, populated_features, expected_features) on any row causes validation failure","Date field not matching YYYY-MM-DD pattern causes schema rejection","Null values in the selected field may affect adjacent change calculations","Invalid field enum value (not 'populated_features' or 'expected_features') causes error"],"whenToPreferThis":"Choose this endpoint when you need fast, local statistical summaries of feature availability records — specifically category frequency counts and row-to-row delta analysis — without requiring any external data lookups, predictions, or ML inference. Ideal for data quality pipelines, feature store audits, and batch monitoring of up to 200 rows at $0.01 per call covering both reports.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:46.672Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-category-frequencies-adjacent-row-1f76ea82"}