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Publication Proofing — String Normalisation + Two-Measure Correlation
x402
Publication Proofing — String Normalisation + Two-Measure Correlation 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).
Normalises strings and computes two-measure correlation across up to 200 publication proofing records (checked_pages, flagged_pages), returning two analytical reports with no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Publication proofing — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied publication proofing records (checked_pages, flagged_pages). 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) normalised string values for document_group and text fields across the submitted rows, and (2) a two-measure correlation analysis between checked_pages and flagged_pages. No external data is fetched; all computation is performed locally on the supplied rows (up to 200).
Example use cases
Editorial team proofing quality audit
“I have 150 rows of proofing logs from our editorial team — each row has a document group, a date in October 2026, and counts of checked pages and flagged pages. Can you normalise the group name strings and run a two-measure correlation between checked_pages and flagged_pages so I can see the relationship?”
Monthly publication QA trend analysis
“Here are our November publication proofing records across four document groups, up to 200 entries, each with a row ID, date, checked_pages, and flagged_pages. Run the string normalisation and two-measure correlation reports so I can understand how flagging rates track with review volume.”
Multi-group document flagging correlation check
“I want to compare checked_pages versus flagged_pages across document groups 'legal-drafts' and 'marketing-copy' for all of Q3 2026. I have about 80 rows total — can you normalise the strings and generate both correlation reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-writing-18-033?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation of publication proofing records and a two-measure correlation between checked_pages and flagged_pages in a single call, with up to 200 rows, at a fixed low cost. It is ideal for editorial QA workflows where you need offline statistical analysis without any external API calls, predictions, or live lookups. Prefer alternatives if you need more than two measures, more than 200 rows, or live data enrichment.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, document_group, date, checked_pages, flagged_pages) cause validation failure
- Date strings not matching YYYY-MM-DD pattern will fail schema validation
- Null values in both numeric fields may prevent meaningful correlation computation
- Insufficient variance in checked_pages or flagged_pages may yield degenerate correlation results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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