{"uid":"cap_GrKMPH38ugDyA0jKug2rp","slug":"join-coverage-analytics-trailing-mean-series-adjacent-row-changes-c74d8b37","name":"Join Coverage Analytics: Trailing Mean Series + Adjacent Row Changes","description":"Join coverage — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes for supplied join coverage records (matched_rows, source_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-09-146?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","matched_rows","source_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"matched_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_rows":1,"matched_rows":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_rows":2,"matched_rows":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_rows":3,"matched_rows":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_rows":4,"matched_rows":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["matched_rows","source_rows"],"type":"string","default":"matched_rows"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_UNCHeQ9L1FXpcDNFJIwdy","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":"Computes trailing moving-average series and adjacent row-to-row delta changes for join coverage metrics (matched_rows and source_rows) across up to 200 supplied data rows.","exampleAgentPrompt":"Can you run trailing mean and adjacent row change analysis on my join coverage data? I have rows with matched_rows and source_rows across groups A and B from October 2026 — analyze the matched_rows field with a window size of 3.","exampleUseCases":[{"title":"Join quality trend monitoring for ETL pipeline","prompt":"I have daily join coverage records from our ETL pipeline — matched_rows and source_rows per dataset group — and I want to see the trailing mean over a window of 7 days plus the day-over-day changes for matched_rows. Can you run those two analytics reports on the last 90 rows?"},{"title":"Detecting anomalous coverage drops between groups","prompt":"Here are 50 join coverage records split across two dataset groups. Can you compute the adjacent row changes for source_rows with a window of 4 so I can spot where coverage suddenly dropped between consecutive days?"},{"title":"Rolling average baseline for join coverage QA","prompt":"I'm doing a QA review of our data join process and need a rolling average baseline. Please analyze these 120 join coverage rows with matched_rows as the field and a trailing window of 5, and also show me the row-to-row changes so I can flag outliers."}],"resultDescription":"Returns two independent analytical reports: (1) a trailing mean series computed over the specified window for the chosen field (matched_rows or source_rows), and (2) adjacent row-to-row change values for the same field — both derived purely from the supplied row data without any external lookups or predictions.","failureModes":["Fewer than 1 row supplied — validation error","More than 200 rows supplied — rejected with max-items error","Window size larger than number of supplied rows — may yield sparse or empty trailing mean results","Invalid date format (not YYYY-MM-DD) — schema validation error","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error","Null numeric values in the selected field — may produce null/undefined entries in the output series"],"whenToPreferThis":"Choose this endpoint when you have pre-collected join coverage records (matched_rows, source_rows) and need both a rolling/trailing mean time series and adjacent delta analysis in a single call for up to 200 rows, without needing live data lookups or external predictions. It is especially suitable for data engineers auditing ETL join quality over time, or analysts building QA dashboards on coverage metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T01:00:13.056Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-trailing-mean-series-adjacent-row-changes-c74d8b37"}