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Save Files Analyzer — Adjacent Row Changes & Missing-Value Fill
x402
Save Files Analyzer — Adjacent Row Changes & Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).
Analyzes save file metrics (save_kb, write_ms) by computing adjacent row changes and filling missing values using a chosen method (zero, mean, or median), returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + 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.
Output
Returns two analytical reports: (1) an adjacent row change report showing the delta between consecutive rows for the selected metric field, and (2) a missing-value fill report showing imputed values for null entries using the chosen method (zero, mean, or median). Reports cover all supplied rows up to 200, computed locally with no external lookups.
Example use cases
Detect save size drift across game sessions
“I have a list of 50 game save records with dates, game areas, save_kb, and write_ms — can you compute the adjacent row changes for save_kb and fill any missing values using median? I want to see where save sizes are jumping or dropping between sessions.”
Impute missing write latency metrics
“My game telemetry dataset has some null write_ms values scattered across 120 rows — can you fill those missing write_ms entries using mean imputation and also show me how each row's write_ms changed compared to the previous row?”
Compare save performance across game areas
“Here are 80 save file records from different game areas with dates, save_kb, and write_ms — run the adjacent row change analysis and fill missing save_kb values with zero so I can compare performance trends across group-a and group-b.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-162?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to analyze sequential change patterns in game save file metrics (save_kb, write_ms) and simultaneously impute missing values in a single call for up to 200 rows. It is purpose-built for game telemetry datasets and provides two focused analytical reports at low cost ($0.01 USDC). Prefer it over general-purpose analytics tools when your data already fits the save-file schema (row_id, game_area, date, save_kb, write_ms) and you need both delta analysis and missing-value handling without external data dependencies.
Side effects
- More than 200 rows submitted — request exceeds maxItems limit
- Missing required fields (row_id, game_area, date, save_kb, write_ms) in any row — validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- Invalid field enum value (not save_kb or write_ms) — schema rejection
- Invalid method enum value (not zero, mean, or median) — schema rejection
- Payment not attached or insufficient — x402 payment required error
- Empty rows array — minItems violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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