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Tutorial Completion Data Audit, String Normalisation & Trailing Mean Series
x402
Tutorial Completion Data Audit, String Normalisation & Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-03).
Runs three local analytical reports on tutorial completion row data: record quality audit, string normalisation, and a trailing mean series for starts or completions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Tutorial completion — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied tutorial completion records (starts, completions). 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
Returns three independent reports: (1) a record quality audit flagging issues such as nulls, out-of-range values, and missing fields; (2) a string normalisation report for game_area and other text fields; and (3) a trailing mean series for the selected numeric field ('starts' or 'completions') computed over the specified rolling window. All reports are generated locally against the supplied data, with no external lookups.
Example use cases
Daily tutorial completion data quality check
“I have 150 rows of daily tutorial starts and completions across different game areas — can you run a record quality audit, normalise the game_area strings, and compute a trailing mean for 'starts' with a window of 5 so I can see trends?”
Game area string cleanup and rolling average
“My tutorial dataset has inconsistent game_area labels and I want a 7-day rolling average on completions. Can you audit the records, normalise those strings, and generate the trailing mean series for the 'completions' field with window 7?”
Pre-analysis data validation for tutorial funnel
“Before I pass these tutorial completion records into my dashboard, can you do a quality audit, clean up the string fields, and calculate a trailing mean series with window 4 on 'starts' so I can spot any anomalies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-20-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three standard analytical reports — quality audit, string normalisation, and trailing mean — on tutorial completion data in a single low-cost call, without requiring external data access, ML predictions, or real-time lookups. Best suited for batch data hygiene and trend analysis on up to 200 rows of game tutorial engagement data.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, game_area, date, starts, completions) cause row-level errors or rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Window size outside 2–200 range causes validation error
- Field enum value other than 'starts' or 'completions' is rejected
- Empty row array (minItems:1 not met) returns error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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