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Question Bank Coverage — String Normalisation + Adjacent Row Changes
x402
Question Bank Coverage — String Normalisation + Adjacent Row Changes 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).
Performs string normalisation and adjacent row change analysis on question bank coverage records (item_count, objective_count) for up to 200 rows in two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Question bank coverage — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied question bank coverage records (item_count, objective_count). 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) a string normalisation report for course_group labels in the submitted rows, and (2) an adjacent row changes report showing delta values between consecutive rows for the selected numeric field (item_count or objective_count). Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Tracking question bank growth over time
“I have a week's worth of question bank coverage records for two course groups — can you show me how item counts are changing from one day to the next and also normalise the course group name strings across those rows? The field I care about is item_count.”
Auditing objective count consistency across groups
“Please run the adjacent row changes and string normalisation reports on my 50-row question bank coverage dataset focused on objective_count — I need to spot any jumps or inconsistencies between consecutive records and clean up the group labels.”
Comparing pre- and post-revision coverage data
“I've uploaded 120 rows of question bank coverage data before and after a curriculum revision, with item_count and objective_count values per course group per date. Can you compute the adjacent row changes for objective_count and normalise all the course_group strings so I can compare them cleanly?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-18-029?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular question bank coverage records and need both string normalisation of categorical labels and adjacent row delta computation in a single low-cost call. Ideal for education data pipelines where course group naming is inconsistent and you want to detect row-to-row coverage changes without setting up your own analytics infrastructure. Best suited for batch sizes up to 200 rows where you want two reports for $0.01.
Side effects
- Fewer than 1 row submitted — minimum row count not met
- More than 200 rows submitted — exceeds maximum row limit
- Missing required fields (row_id, course_group, date, item_count, objective_count) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — pattern validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — schema rejection
- Invalid field selector (must be 'item_count' or 'objective_count') — enum validation error
- Payment not included or insufficient — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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