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Publication Proofing — Daily Sum Series + Explicit Missing-Value Fill
x402
Publication Proofing — Daily Sum Series + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Computes daily sum time series and fills missing values (zero, mean, or median) for publication proofing records tracking checked and flagged pages, across up to 200 rows with two independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Publication proofing — Daily sum series + Explicit missing-value fill: Daily sum series + Explicit missing-value fill 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 independent analytical reports: (1) a daily sum time series aggregating the selected field (checked_pages or flagged_pages) by date, and (2) an explicit missing-value fill report replacing nulls with the chosen method (zero, mean, or median). Both reports are computed locally from the supplied rows with no external lookups.
Example use cases
Weekly proofreading volume summary
“Here are my proofreading logs for October — each row has a date, document group, and how many pages were checked and flagged. Can you compute the daily totals for flagged_pages and fill any missing entries with zero so I can see the full week at a glance?”
Editorial QA missing-data imputation
“I'm auditing our editorial pipeline and some days are missing checked_pages counts. Can you run the daily sum series on my dataset and use median imputation to fill the gaps — I have about 150 rows across three document groups?”
Document group flagging trend analysis
“I want to compare flagging rates across document groups over the last month. Can you generate daily sum reports for flagged_pages across my proofing records, filling any nulls with the mean, and give me two independent reports from the same data?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-writing-18-145?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have publication proofing records (checked_pages, flagged_pages) and need both a daily sum time series and explicit missing-value imputation in a single low-cost call. It is ideal for editorial or document QA workflows where datasets are small (≤200 rows), no external data lookups are needed, and the analysis is entirely self-contained on supplied data.
Side effects
- Rows exceed 200-item limit — request rejected
- Date field does not match YYYY-MM-DD pattern — validation error
- Both checked_pages and flagged_pages are null for all rows — imputation produces no meaningful output
- Invalid fill method enum value — schema validation error
- row_id missing or empty — required field error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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