{"uid":"cap_NI2JgFZIFY0cPwdoybKYo","slug":"feature-availability-analytics-daily-sum-series-adjacent-row-changes-7dbd4b79","name":"Feature Availability Analytics — Daily Sum Series + Adjacent Row Changes","description":"Feature availability — Daily sum series + Adjacent row changes: Daily sum series + 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-10-137?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_6jxWFa0vX8ItbXPelTX1H","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 two analytical reports on feature availability data: a daily sum time series and adjacent row change detection, for up to 200 rows of populated/expected feature counts.","exampleAgentPrompt":"Can you run the daily sum series and adjacent row change reports on my feature availability data? I have rows with date, dataset_group, row_id, populated_features, and expected_features — up to 50 records — and I want to analyze the populated_features field.","exampleUseCases":[{"title":"Daily feature completeness trend audit","prompt":"I have 120 rows of feature availability records with dates, dataset groups, and populated vs expected feature counts — can you compute the daily sum series and show me how the populated_features totals change day over day?"},{"title":"Spotting sudden drops in populated features","prompt":"Run the adjacent row change report on my feature availability data so I can see where populated_features jumped or dropped significantly between consecutive records — here are the rows with their dates and dataset groups."},{"title":"Comparing expected vs populated feature volumes","prompt":"I want to analyze expected_features instead of populated_features — can you run both the daily sum series and the adjacent row change reports on my 80 feature availability records to see how expected volumes trend over the date range?"}],"resultDescription":"Two analytical report outputs computed locally from the supplied rows: (1) a daily sum series aggregating the selected field (populated_features or expected_features) by date, and (2) an adjacent row change report showing differences between consecutive rows — all derived from the provided data with no external lookups.","failureModes":["Fewer than 1 row supplied — minimum array size is 1","More than 200 rows supplied — maximum exceeded","Missing required fields (row_id, dataset_group, date, populated_features, expected_features) on any row","Invalid date format — must match YYYY-MM-DD pattern","Numeric values outside allowed range (±1,000,000,000,000)","Invalid field enum value — must be 'populated_features' or 'expected_features'","Payment not provided or insufficient — $0.01 USDC required"],"whenToPreferThis":"Choose this endpoint when you have up to 200 feature availability records with date, group, and numeric feature counts and need both a daily aggregated time series and adjacent row change detection in a single low-cost call. It is best for offline/supplied-data analysis where no live lookup or external data enrichment is needed. Prefer alternatives if you need more than 200 rows, real-time data fetching, prediction, or more than two report types.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:12:24.088Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-daily-sum-series-adjacent-row-changes-7dbd4b79"}