{"uid":"cap_tfPRr0QuPaI9bNvzL8YHW","slug":"save-files-daily-sum-series-with-missing-value-fill-aa415b49","name":"Save Files Daily Sum Series with Missing-Value Fill","description":"Save files — Daily sum series + Explicit missing-value fill: Daily sum series + Explicit missing-value fill for supplied save files records (save_kb, write_ms). 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-game-13-145?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","game_area","date","save_kb","write_ms"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"save_kb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"write_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"game_area":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","save_kb":10,"write_ms":1,"game_area":"group-a"},{"date":"2026-10-02","row_id":"row-2","save_kb":20,"write_ms":2,"game_area":"group-b"},{"date":"2026-10-03","row_id":"row-3","save_kb":15,"write_ms":3,"game_area":"group-a"},{"date":"2026-10-04","row_id":"row-4","save_kb":30,"write_ms":4,"game_area":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["save_kb","write_ms"],"type":"string","default":"save_kb"},"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_nsKcvdJWvXrGQr7orauK-","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 daily sum time series for save file metrics (save_kb or write_ms) and fills missing values using zero, mean, or median imputation — returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have save file records for my game across two groups from October 1–4, 2026 with save_kb values — can you compute the daily sum series for save_kb and fill any missing values using median imputation?","exampleUseCases":[{"title":"Daily save size trend by game area","prompt":"I have save file logs for group-a and group-b across about 150 rows with dates and save_kb values — can you compute the daily total save_kb for each day and fill any missing days using median fill?"},{"title":"Write latency daily aggregation with null fill","prompt":"I've got write_ms records from our game save system for about 80 rows, some entries have nulls — can you sum write_ms by date and use zero-fill for any missing values?"},{"title":"Game area storage audit with imputation","prompt":"Here are 200 rows of save file events tagged by game_area and date, with save_kb recorded — I need the daily sum series for save_kb with mean imputation for any gaps in the data."}],"resultDescription":"Returns two analytical reports per call (for up to 200 input rows): a daily sum time series of the chosen metric (save_kb or write_ms), and the same series with missing values explicitly filled using the chosen method (zero, mean, or median). Reports are computed locally with no external lookups.","failureModes":["Fewer than 1 or more than 200 rows provided — request rejected","date field does not match YYYY-MM-DD format — validation error","field enum value not one of 'save_kb' or 'write_ms' — invalid input","method enum not one of 'zero', 'mean', 'median' — invalid input","All values for the selected field are null — mean/median imputation may be undefined","numeric values outside allowed range (-1e12 to 1e12) — rejected"],"whenToPreferThis":"Choose this endpoint when you need to aggregate save file performance data (save sizes in KB or write latencies in ms) into a daily sum time series and want explicit missing-value imputation built into the same call. Ideal for game analytics pipelines where save event logs contain nulls and you need clean daily totals without a separate ETL step. Not suitable for live lookups, predictions, or datasets larger than 200 rows per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T12:38:09.931Z","isFirstParty":false,"canonicalSlug":"save-files-daily-sum-series-with-missing-value-fill-aa415b49"}