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Save Files Data Quality Audit — Record Quality, String Normalisation & Category Frequencies
x402
Save Files Data Quality Audit — Record Quality, String Normalisation & Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Runs three analytical reports (record quality audit, string normalisation, and category frequencies) on up to 200 rows of game save-file data (save_kb, write_ms) using local engines with no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied save files records (save_kb, write_ms). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three structured reports returned for the submitted rows: (1) a record quality audit flagging missing, null, or out-of-range values in each row; (2) a string normalisation report standardising text fields like game_area; (3) a category frequency table counting occurrences of each unique game_area value across the supplied dataset. All computed locally with no external data.
Example use cases
Game save log quality check
“I have a batch of game save events — each has a row ID, a game area name, a date, a save size in KB, and a write time in milliseconds. Can you audit the record quality, normalise any messy string fields, and give me a frequency breakdown by game area for all 200 rows?”
Detecting bad values in save data
“Some of my save file records might have nulls or weird values in save_kb and write_ms. Can you run a quality audit on these rows and flag any issues, plus tell me how the game_area categories are distributed?”
Profiling a new save dataset
“Before I use this dataset of game save logs, I want to profile it — can you check record completeness, standardise the game_area strings, and count how often each category shows up? I'll send you up to 150 rows with row_id, date, save_kb, write_ms, and game_area.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data quality audit, string normalisation, and category frequency analysis for game save file records in a single low-cost API call. It is best suited for datasets up to 200 rows where the fields are save_kb, write_ms, game_area, date, and row_id. Prefer this over generic data profiling tools when working specifically with game save metrics and wanting three distinct analytical outputs without any external lookups or predictions.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, game_area, date, save_kb, write_ms) per row causes rejection
- Invalid date format (non-YYYY-MM-DD) triggers schema validation failure
- Numeric values outside allowed range cause rejection
- Empty rows array returns an error
- Payment of 0.01 USDC not included or declined results in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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