{"uid":"cap_Kn5yU1_duxZspPukwqDlz","slug":"duplicate-observations-numeric-interval-filter-trailing-mean-series-ad3e2a7a","name":"Duplicate Observations — Numeric Interval Filter + Trailing Mean Series","description":"Duplicate observations — Numeric interval filter + Trailing mean series: Numeric interval filter + Trailing mean series for supplied duplicate observations records (duplicate_rows, total_rows). 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-04-126?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","duplicate_rows","total_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"duplicate_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_rows":1,"dataset_group":"group-a","duplicate_rows":10},{"date":"2026-10-02","row_id":"row-2","total_rows":2,"dataset_group":"group-b","duplicate_rows":20},{"date":"2026-10-03","row_id":"row-3","total_rows":3,"dataset_group":"group-a","duplicate_rows":15},{"date":"2026-10-04","row_id":"row-4","total_rows":4,"dataset_group":"group-b","duplicate_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["duplicate_rows","total_rows"],"type":"string","default":"duplicate_rows"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_nReAQDqCJhUklWCyItXzl","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":"Applies a numeric interval filter and computes a trailing mean series on up to 200 duplicate-observation records, returning two analytical reports from local engines.","exampleAgentPrompt":"I have 150 rows of duplicate observation records with columns row_id, dataset_group, date, duplicate_rows, and total_rows — can you run a numeric interval filter (min 0, max 500) on the duplicate_rows field and compute a trailing mean with a window of 7?","exampleUseCases":[{"title":"Daily data quality monitoring report","prompt":"Here are my dataset quality records for October — each row has a date, group name, and duplicate_row counts. Please filter the duplicate_rows field between 0 and 200 and compute a trailing mean series with a window of 5 so I can spot trends."},{"title":"Detecting duplicate spikes in ingestion pipeline","prompt":"I've got 180 rows of ingestion pipeline stats with duplicate_rows and total_rows per day per group. Run an interval filter keeping values between 10 and 1000 on total_rows and give me the trailing mean with a window of 14."},{"title":"Comparing duplicate rates across dataset groups","prompt":"Can you analyze this batch of 60 records from two dataset groups — filter out duplicate_rows values outside the 0 to 100 range and show me the rolling average with a window size of 3?"}],"resultDescription":"Returns two analytical reports: (1) a numeric interval filter report showing which rows fall within the specified min/max bounds for the chosen field, and (2) a trailing mean series report showing rolling averages computed over the specified window size — both generated locally without external lookups.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Window size larger than number of rows — trailing mean may have insufficient data","Invalid date format (not YYYY-MM-DD) — schema validation error","Null values in the target field — may affect filter and mean calculations","Min value greater than max value — empty interval filter result","Missing required fields (row_id, date, duplicate_rows, total_rows) — request rejected"],"whenToPreferThis":"Choose this endpoint when you have up to 200 structured duplicate-observation records and need both a bounded numeric filter and a rolling/trailing mean series in a single low-cost call ($0.01 USDC). It is ideal for data quality monitoring workflows where you want to isolate records within a value range and simultaneously understand temporal trends via a sliding window — all processed locally without any external API dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:45.786Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-numeric-interval-filter-trailing-mean-series-ad3e2a7a"}