{"uid":"cap_8ym5O7MMIsbkC0U2uGNBv","slug":"save-files-analyzer-string-normalisation-date-by-category-pivot-a52ecf9e","name":"Save Files Analyzer — String Normalisation + Date-by-Category Pivot","description":"Save files — String normalisation + Date by category pivot: String normalisation + 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-13-035?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"}}},"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_2Qyh9Pjinmmq2tigynihz","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":"Runs two analytical reports on game save-file records: string normalisation of categorical fields and a date-by-category pivot for a chosen numeric metric (save_kb or write_ms), on up to 200 rows.","exampleAgentPrompt":"I have a batch of up to 200 game save records with fields row_id, game_area, date, save_kb, and write_ms — can you run the string normalisation and date-by-category pivot reports on them using save_kb as the metric?","exampleUseCases":[{"title":"Game save size trends by area","prompt":"I've got 150 save file records with columns row_id, game_area, date, and save_kb. Can you normalize the game_area strings and pivot the save_kb values by date and game_area so I can see trends?"},{"title":"Write latency breakdown by category","prompt":"Run the two analytical reports on my save file data — string normalisation on game_area and a date-by-category pivot using write_ms as the metric. I have around 80 rows."},{"title":"Clean and summarize mixed save records","prompt":"My game save export has inconsistent area labels and I want to see how save_kb stacks up across dates and categories. Can you normalize the strings and generate the date pivot report for my 200 rows?"}],"resultDescription":"Two structured reports: (1) a string-normalised view of the game_area categorical field across all supplied rows, and (2) a date-by-category pivot table showing aggregated values of the chosen numeric field (save_kb or write_ms) broken down by date and game_area. All computation is performed locally on the supplied data — no external lookups or predictions.","failureModes":["Rows exceed 200-item limit — request rejected","Missing required fields (row_id, game_area, date, save_kb, write_ms) — validation error","Invalid date format (must match YYYY-MM-DD) — schema rejection","Numeric values outside allowed range — validation failure","Empty rows array — minimum 1 item required","Invalid field selector (must be save_kb or write_ms) — enum validation error"],"whenToPreferThis":"Choose this endpoint when you have game save-file records (up to 200 rows) and need both string normalisation of categorical labels and a date-by-category pivot on a numeric metric in a single low-cost call. It is ideal for lightweight, offline analytical summaries of save performance data without needing a full data warehouse or live lookup service.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T19:09:48.440Z","isFirstParty":false,"canonicalSlug":"save-files-analyzer-string-normalisation-date-by-category-pivot-a52ecf9e"}