{"uid":"cap_VIAKFHe8hOHrYGTts9oB-","slug":"feature-availability-duplicate-key-report-date-by-category-pivot-97101d7f","name":"Feature Availability Duplicate Key Report + Date by Category Pivot","description":"Feature availability — Duplicate key report + Date by category pivot: Duplicate key report + Date by category pivot 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-10-068?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_Uttncc1Vrr6j-94yxCNyG","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":"Generates two local analytical reports — a duplicate key report and a date-by-category pivot — for supplied feature availability records (populated_features, expected_features) across up to 200 rows.","exampleAgentPrompt":"Can you run a duplicate key report and a date-by-category pivot on my feature availability data? I have rows with row_id, dataset_group, date, populated_features, and expected_features — here are up to 200 rows, and I want to analyze the populated_features field.","exampleUseCases":[{"title":"Data quality check for ML pipeline","prompt":"I have a dataset of feature availability records with row IDs, dataset groups, dates, and counts of populated vs expected features — can you run a duplicate key report on them so I can catch any repeated row_ids before I feed this into my ML pipeline?"},{"title":"Date-category pivot for feature completeness review","prompt":"Can you pivot my feature availability records by date and dataset group so I can see how populated_features breaks down across different groups over time? I've got about 150 rows across group-a and group-b spanning October 2026."},{"title":"Audit expected vs populated features","prompt":"I need to audit my feature availability dataset — run both a duplicate key report and a date-by-category pivot on these records so I can spot data gaps and any duplicated entries in one go. The field I care about is expected_features."}],"resultDescription":"Returns two independent analytical reports: (1) a duplicate key report identifying any repeated row_id values within the supplied records, and (2) a date-by-category pivot table breaking down the selected field (populated_features or expected_features) by date and dataset_group — all computed locally without external lookups, covering up to 200 input rows.","failureModes":["Empty or missing rows array returns an error","More than 200 rows submitted exceeds the maxItems limit and will be rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","row_id missing or empty string (minLength:1) causes validation error","populated_features or expected_features values outside ±1,000,000,000,000 range are rejected","Invalid field enum value (only 'populated_features' or 'expected_features' accepted)"],"whenToPreferThis":"Choose this endpoint when you have a batch of up to 200 feature availability records and need both a duplicate key integrity check and a date-by-category pivot in a single low-cost call ($0.01 USDC). Ideal for data quality audits on feature engineering pipelines where you want local analytical computation with no external API dependencies or live data lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:39.963Z","isFirstParty":false,"canonicalSlug":"feature-availability-duplicate-key-report-date-by-category-pivot-97101d7f"}