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Editorial Review Data Audit: Record Quality, String Normalisation & Numeric Row Ordering
x402
Editorial Review Data Audit: Record Quality, String Normalisation & Numeric Row Ordering 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).
Audits editorial review records (comment_count, resolved_comments) for data quality issues, normalises string fields, and sorts rows numerically — delivering 3 independent reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Editorial review — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering for supplied editorial review records (comment_count, resolved_comments). 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 independent analytical reports: (1) a record quality audit flagging issues in the supplied editorial review rows, (2) a string normalisation report for text fields such as document_group, and (3) a numeric row ordering report sorting rows by the selected field (comment_count or resolved_comments). All processing is done locally on the supplied data with no external lookups.
Example use cases
Audit document review comment data
“I've got a batch of editorial review records with comment counts and resolved comments across a few document groups — can you check them for data quality issues, normalise the text fields, and sort the rows by comment_count? Here are up to 50 rows to process.”
Validate resolved comments before reporting
“Before I submit my weekly editorial report, can you run a quality audit on my resolved_comments data for about 120 rows, normalise the document_group strings, and give me the rows ordered numerically by resolved_comments?”
Clean editorial workflow data export
“I exported editorial review records from our system — 80 rows with date, document_group, comment_count, and resolved_comments. Can you audit the record quality, clean up any string inconsistencies, and return them sorted by comment_count?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-writing-03-196?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have editorial review tabular data (with comment_count and resolved_comments fields) and need a combined data quality audit, string normalisation, and numeric sort in a single low-cost call. It is ideal for pre-reporting data cleaning of document review workflows with up to 200 rows, requiring no external lookups or predictions — purely local analytical processing.
Side effects
- Rows array empty or missing — minimum 1 row required
- More than 200 rows supplied — exceeds maximum batch size
- Invalid date format (must match YYYY-MM-DD pattern)
- row_id missing or empty string — required field
- Numeric fields (comment_count, resolved_comments) out of allowed range (±1,000,000,000,000)
- Invalid field enum value — must be 'comment_count' or 'resolved_comments'
- Malformed JSON body — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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