{"uid":"cap_UERJzhoMeOh30N6zMEdI_","slug":"feature-availability-analytics-exact-row-deduplication-trailing-mean-5fbb07a1","name":"Feature Availability Analytics — Exact Row Deduplication + Trailing Mean Series","description":"Feature availability — Exact row deduplication + Trailing mean series: Exact row deduplication + Trailing mean 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-10-045?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"},"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_50cPyJ1o7PsJQlKTJJuY6","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":"Performs exact row deduplication and trailing mean series analysis on feature availability records (populated_features, expected_features) for up to 200 rows.","exampleAgentPrompt":"Deduplicate my feature availability rows and compute a trailing mean series with a window of 3 for the populated_features field — I have 4 records for groups group-a and group-b from 2026-10-01 to 2026-10-04.","exampleUseCases":[{"title":"ML feature completeness trend analysis","prompt":"I have 150 daily feature availability records across multiple dataset groups and want to deduplicate them and then see a trailing mean with window 7 for populated_features so I can spot trends in feature completeness over time."},{"title":"Data pipeline duplicate audit","prompt":"Can you remove exact duplicate rows from my feature availability dataset (about 80 records) and also give me a rolling mean of expected_features with a window of 5 so I can see if our pipeline is stable?"},{"title":"Dataset group feature coverage smoothing","prompt":"I'm monitoring how many features are populated versus expected across two dataset groups daily. Deduplicate my 200-row log and compute a trailing mean series with window 4 on populated_features so I can track coverage smoothly."}],"resultDescription":"Returns two independent analytical reports: (1) a deduplicated version of the submitted rows with exact duplicates removed, and (2) a trailing mean series computed over the specified field (populated_features or expected_features) using the given window size — both derived purely from the supplied data with no external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Invalid date format (must be YYYY-MM-DD) — validation error","Window size outside range 2–200 — rejected","Missing required fields (row_id, date, populated_features, expected_features) — schema error","Numeric values out of allowed range (-1e12 to 1e12) — validation error"],"whenToPreferThis":"Choose this endpoint when you need both exact row deduplication and a trailing mean series on feature availability data in a single low-cost call ($0.01 USDC). Ideal for data quality pipelines that track feature population completeness across dataset groups over time, especially when you want two analytical passes (dedup + smoothing) on up to 200 rows without external dependencies or live data lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:40.549Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-exact-row-deduplication-trailing-mean-5fbb07a1"}