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Peer Feedback String Normalisation + Numeric Interval Filter
x402
Peer Feedback String Normalisation + Numeric Interval Filter is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Normalises string fields and filters rows by a numeric interval for peer feedback records containing review_count and actioned_comments fields, returning up to 2 analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Peer feedback — String normalisation + Numeric interval filter: String normalisation + Numeric interval filter for supplied peer feedback records (review_count, actioned_comments). 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 up to 2 analytical reports per call covering: (1) normalised string fields for course_group and related text columns, and (2) rows filtered to only those where the chosen numeric field (review_count or actioned_comments) falls within the specified [minimum, maximum] interval, for up to 200 input rows.
Example use cases
Filter reviews by activity threshold
“I have a set of peer feedback records for my online courses and I want to keep only the rows where review_count falls between 5 and 50 — can you normalize the course group names and apply that interval filter across my 120 rows?”
Validate actioned comments range
“Can you run my 80 peer feedback rows through string normalization on the course_group field and filter so I only see records where actioned_comments is between 1 and 10?”
Education cohort feedback cleanup
“I've got peer review data from two course cohorts — normalize the group name strings and give me only the rows where review_count is between 0 and 30, using my 50 feedback records.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-23-026?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have peer feedback or education review datasets (up to 200 rows) that need both string normalization on categorical fields like course_group and numeric range filtering on review_count or actioned_comments. It is ideal for lightweight, offline analytical preprocessing of structured tabular feedback data without needing a live database, prediction model, or external lookup service.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required row fields (row_id, date, review_count, actioned_comments) cause validation errors
- Date field not matching YYYY-MM-DD pattern causes schema rejection
- minimum greater than maximum results in empty filtered output or error
- Null numeric values in review_count or actioned_comments may be excluded from interval filtering
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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