{"uid":"cap_u1Jc3a3cO4srDIkn_cG-W","slug":"feature-availability-analytics-exact-row-deduplication-daily-sum-series-c3fb60ef","name":"Feature Availability Analytics — Exact Row Deduplication + Daily Sum Series","description":"Feature availability — Exact row deduplication + Daily sum series: Exact row deduplication + Daily sum series 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-10-044?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_ogp0HH3wI-fLoWpjURsER","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":"Deduplicates exact rows and computes a daily sum time series for feature availability records (populated_features and expected_features) across up to 200 rows.","exampleAgentPrompt":"I have up to 200 rows of feature availability data with date, row_id, dataset_group, populated_features, and expected_features — can you deduplicate the exact rows and also generate a daily sum series for populated_features across all groups?","exampleUseCases":[{"title":"Daily feature coverage rollup","prompt":"I've got a list of up to 150 feature availability records with date, row_id, dataset_group, populated_features, and expected_features — please give me a daily sum series for expected_features and also remove any exact duplicate rows so I know what's actually unique."},{"title":"Data pipeline deduplication audit","prompt":"My ETL pipeline sometimes writes duplicate rows into our feature availability table. Can you take these 200 rows with row_id, date, dataset_group, populated_features, and expected_features and run exact row deduplication plus a daily sum on populated_features so I can see both the clean set and the daily totals?"},{"title":"Feature completeness trend analysis","prompt":"I want to understand how feature completeness changes day by day across two dataset groups — group-a and group-b. Here are my feature availability records with date, row_id, populated_features, and expected_features. Can you deduplicate exact rows and show me the daily sum series for populated_features?"}],"resultDescription":"Returns two independent analytical reports: (1) an exact row deduplication report identifying and removing fully duplicate rows from the supplied dataset, and (2) a daily sum series aggregating the selected field (populated_features or expected_features) by date. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Missing required fields (row_id, date, populated_features, expected_features) will cause validation errors","Submitting more than 200 rows exceeds the maxItems limit and will be rejected","Invalid date format (not YYYY-MM-DD) will fail pattern validation","Null values in populated_features or expected_features may affect sum computations","Empty rows array (minItems: 1 not met) will be rejected"],"whenToPreferThis":"Choose this endpoint when you need both exact row deduplication AND a daily sum time series for feature availability data in a single low-cost call ($0.01 USDC). Ideal for data quality pipelines where duplicate rows may have been ingested and you also want a temporal aggregate view. Best suited for small-to-medium datasets (up to 200 rows) processed analytically without any external data lookups or live predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:43.512Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-exact-row-deduplication-daily-sum-series-c3fb60ef"}