{"uid":"cap_gqI0J-AZwZCfze_QIw3L7","slug":"save-files-analytics-base-100-series-explicit-missing-value-fill-80452263","name":"Save Files Analytics — Base-100 Series + Explicit Missing-Value Fill","description":"Save files — Base-100 series + Explicit missing-value fill: Base-100 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-13-169?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"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_WM46G1l2OGrXNf9aEzo4-","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 a base-100 indexed time series and fills missing values (zero, mean, or median) for save file metrics (save size in KB and write duration in ms) across up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"I have game save file records for two areas (group-a and group-b) from October 2026 — up to 50 rows with some null save_kb values. Can you normalize the save_kb field to a base-100 index starting from row 0 and fill any missing values using median imputation?","exampleUseCases":[{"title":"Game save size trend analysis","prompt":"I've got 120 rows of daily save file records across three game areas from January 2026, some with missing save_kb values — can you index the save_kb field to base-100 starting from row 0 and fill the blanks using median so I can compare growth trends?"},{"title":"Write speed normalization across regions","prompt":"Can you take my 80-row dataset of game save write times (write_ms) across different zones, fill any null write_ms entries with the mean, and produce a base-100 indexed series starting from the first row?"},{"title":"Missing data audit before save performance report","prompt":"Before I finalize my save performance report, can you run my 200-row save file dataset through base-100 normalization on save_kb, using zero-fill for any missing values, anchored at base index row 5?"}],"resultDescription":"Returns 2 analytical reports: (1) a base-100 indexed time series for the selected field (save_kb or write_ms) anchored at the specified base_index row, and (2) the same series with missing values explicitly filled using the chosen method (zero, mean, or median). Results are computed locally with no external lookups.","failureModes":["Fewer than 1 row provided — schema requires minItems: 1","More than 200 rows submitted — exceeds maxItems: 200 limit","Invalid date format — must match YYYY-MM-DD pattern","base_index out of range — must be between 0 and 199 and within actual row count","Invalid field value — must be 'save_kb' or 'write_ms'","Invalid method value — must be 'zero', 'mean', or 'median'","All values for selected field are null — mean/median imputation undefined"],"whenToPreferThis":"Choose this endpoint when you have tabular game save file data (save size or write duration) with potential missing values and need both a base-100 normalized index series and an imputed series in a single call. Ideal for game analytics pipelines comparing save performance across areas over time. Not suitable for live data fetching, prediction, or non-game-file datasets.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-06T00:34:12.581Z","isFirstParty":false,"canonicalSlug":"save-files-analytics-base-100-series-explicit-missing-value-fill-80452263"}