{"uid":"cap_vxKxNYP3iJ_KNMrQj0OL9","slug":"save-files-numeric-distribution-and-adjacent-row-changes-analyzer-5f8cae10","name":"Save Files Numeric Distribution and Adjacent Row Changes Analyzer","description":"Save files — Numeric distribution + Adjacent row changes: Numeric distribution + Adjacent row changes 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-13-077?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_ChkMlUSaVz5MLp6Dd3yyO","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":"Analyzes save file records (save_kb, write_ms) to produce two local analytical reports: a numeric distribution summary and an adjacent row change report, for up to 200 rows.","exampleAgentPrompt":"Can you analyze the distribution of save_kb values and show me how they change between adjacent rows for my save file records? Here's my data: row-1 on 2026-10-01 in group-a had save_kb=10 and write_ms=1, row-2 on 2026-10-02 in group-b had save_kb=20 and write_ms=2, row-3 on 2026-10-03 in group-a had save_kb=15 and write_ms=3 — run the numeric distribution and adjacent row changes reports on the save_kb field.","exampleUseCases":[{"title":"Spotting save size spikes between game sessions","prompt":"I have a list of game save records with dates, game areas, save_kb, and write_ms values — can you run a numeric distribution and adjacent row change analysis on the save_kb field to see if there are any big jumps between sessions? Here are 8 rows from group-a and group-b across October 2026."},{"title":"Profiling write latency across game areas","prompt":"Can you analyze the write_ms values in my save file dataset for numeric distribution patterns and show me how write time changes between consecutive rows? I have about 50 records from different game areas dated throughout 2026 — use write_ms as the field."},{"title":"QA check on save file data consistency","prompt":"I want to validate my save file records by looking at how save_kb is distributed and whether there are unusual jumps between adjacent rows — can you run both the distribution and adjacent row changes reports on this batch of 120 rows tagged with game areas and dates?"}],"resultDescription":"Returns two analytical reports: (1) a numeric distribution breakdown of the selected field (save_kb or write_ms) across up to 200 rows, showing spread, frequency, and distributional characteristics; and (2) an adjacent row changes report showing differences between consecutive row values, highlighting sequential variation in the save file metric.","failureModes":["Missing required fields (row_id, game_area, date, save_kb, write_ms) causes validation error","More than 200 rows submitted exceeds the maxItems limit and is rejected","Invalid date format (not YYYY-MM-DD) fails schema validation","Field value outside numeric bounds causes rejection","Empty rows array returns an error","Non-numeric values in save_kb or write_ms (when not null) cause computation failure"],"whenToPreferThis":"Choose this endpoint when you have tabular game save file records with save_kb and write_ms fields and need both a distributional overview and sequential row-to-row change detection in a single low-cost call. It is best suited for offline batch analysis of up to 200 rows without needing external lookups, predictions, or real-time data. Prefer alternatives if you need trailing averages, IQR outlier flagging, grouped summaries, or analyses of non-save-file datasets.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:31:54.712Z","isFirstParty":false,"canonicalSlug":"save-files-numeric-distribution-and-adjacent-row-changes-analyzer-5f8cae10"}