{"uid":"cap_fXupPZGry02J2biRcC3A0","slug":"product-feature-telemetry-explicit-missing-value-fill-f565c280","name":"Product Feature Telemetry – Explicit Missing-Value Fill","description":"Product feature telemetry - Explicit missing-value fill: Explicit missing-value fill for Product feature telemetry. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded explicit missing-value fill report.","url":"https://market.datapackvibe.com/x402/assistant-ar-analytics-feature-telemetry-fill-missing?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","feature","date","events","unique_users"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"events":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"feature":{"type":"string","default":"","maxLength":80},"unique_users":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","events":10,"row_id":"row-1","feature":"group-a","unique_users":1},{"date":"2026-10-02","events":20,"row_id":"row-2","feature":"group-b","unique_users":2},{"date":"2026-10-03","events":15,"row_id":"row-3","feature":"group-a","unique_users":3},{"date":"2026-10-04","events":30,"row_id":"row-4","feature":"group-b","unique_users":4}],"maxItems":200,"minItems":1},"field":{"enum":["events","unique_users"],"type":"string","default":"events"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_gqB3sBjzXzLrvg41Y_VUh","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":"Fills missing numeric values (events or unique_users) in product feature telemetry records using zero, mean, or median imputation, returning a bounded local analytics report.","exampleAgentPrompt":"I have product feature telemetry rows with some null events values — can you fill those missing values using median imputation across up to 200 records grouped by feature name and date?","exampleUseCases":[{"title":"Clean feature usage data before reporting","prompt":"My feature telemetry data has some null events values scattered across about 50 rows for features like 'search-bar' and 'dashboard' over the past month — fill those missing events values using median so I can run a clean usage report."},{"title":"Zero-fill missing unique_users for a dashboard","prompt":"I need to zero-fill the missing unique_users values in my product telemetry records before displaying them on a dashboard — I have about 100 rows with dates and feature names, can you apply zero fill on the unique_users field?"},{"title":"Impute mean events for A/B group comparison","prompt":"I'm comparing two feature groups, 'group-a' and 'group-b', but some of the event counts are null — please fill those missing events values using mean imputation on my 80 telemetry rows so I can do a fair comparison."}],"resultDescription":"Returns a bounded missing-value fill report containing the processed telemetry rows with null values replaced according to the chosen method (zero, mean, or median), along with local descriptive analytics for the filled dataset. No external enrichment or forecasting is included.","failureModes":["Rows array is empty or exceeds 200 items — validation error returned","Invalid date format (not YYYY-MM-DD) in any row — schema rejection","Unknown field value (not 'events' or 'unique_users') — enum validation error","Unknown method value (not 'zero', 'mean', 'median') — enum validation error","Missing required fields (row_id, feature, date) in any row — schema error","Payment of 0.01 USDC not provided — x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you need fast, local imputation of missing numeric values in product feature telemetry data (up to 200 rows) using simple statistical methods (zero, mean, or median) without any external data enrichment or forecasting. It is ideal for lightweight data-cleaning steps prior to analytics or reporting pipelines where cost, speed, and determinism matter.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T18:48:38.434Z","isFirstParty":false,"canonicalSlug":"product-feature-telemetry-explicit-missing-value-fill-f565c280"}