{"uid":"cap_qAs0FDWTDcejzUJTgYXNW","slug":"save-files-analytics-numeric-interval-filter-date-by-category-pivot-adaedf0c","name":"Save Files Analytics — Numeric Interval Filter + Date by Category Pivot","description":"Save files — Numeric interval filter + Date by category pivot: Numeric interval filter + Date by category pivot 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-13-133?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_7tzvuBzCP2RYT5-SgNDu3","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":"Filters save-file records by a numeric interval and produces a date-by-category pivot table, returning two analytical reports for up to 200 rows of save_kb/write_ms data.","exampleAgentPrompt":"Can you run a numeric interval filter and date-by-category pivot on these save file records, filtering save_kb between 10 and 50, and group the results by game_area across dates? I have 4 rows: row-1 on 2026-10-01 with save_kb=10 and write_ms=1 in group-a, row-2 on 2026-10-02 with save_kb=20 and write_ms=2 in group-b, row-3 on 2026-10-03 with save_kb=15 and write_ms=3 in group-a, row-4 on 2026-10-04 with save_kb=30 and write_ms=4 in group-b.","exampleUseCases":[{"title":"Game area save size breakdown","prompt":"I have 50 rows of game save records with columns row_id, date, game_area, save_kb, and write_ms. Can you filter them to only saves between 5 and 100 KB on the save_kb field, then pivot the results by date and game_area so I can see which areas are generating the most save data?"},{"title":"Write latency analysis by category","prompt":"Run an interval filter and date-by-category pivot on my game save logs using the write_ms field, keeping only records where write_ms is between 1 and 50. My data has 20 rows across areas like dungeon, overworld, and hub, dated from 2026-09-01 to 2026-09-20."},{"title":"Save performance report for QA review","prompt":"I need two reports on my save file test data — filter rows where save_kb is between 0 and 30, and then pivot the filtered data by date and game_area. Here are my 8 rows covering game areas alpha and beta from 2026-11-01 to 2026-11-08."}],"resultDescription":"Returns two analytical reports: (1) a numeric interval filter report showing which rows fall within the specified min/max range for the chosen field (save_kb or write_ms), and (2) a date-by-category pivot table aggregating values across game_area and date dimensions. Results cover up to 200 input rows and are computed locally without external lookups.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Missing required fields (row_id, game_area, date, save_kb, write_ms) on any row — validation error","Date values not matching YYYY-MM-DD pattern — schema validation failure","Minimum value greater than maximum value — may produce empty filter result","Null numeric values may be excluded from interval filter calculations"],"whenToPreferThis":"Choose this endpoint when you have game save file records with save_kb and write_ms metrics and need both a numeric range filter and a date-by-category pivot in a single paid call. It is ideal for game analytics pipelines that need to segment save performance by area and time period without setting up a local data warehouse. At $0.01 USDC for two reports on up to 200 rows, it is cost-effective for small-to-medium batch analytical jobs on structured save data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-06T00:34:04.555Z","isFirstParty":false,"canonicalSlug":"save-files-analytics-numeric-interval-filter-date-by-category-pivot-adaedf0c"}