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Newsletter Observations Analyzer — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Newsletter Observations Analyzer — Record Quality Audit + String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-03).
Runs three analytical reports (record quality audit, string normalisation, and trailing mean series) on newsletter observation data (delivered_messages, open_count) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Newsletter observations — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied newsletter observations records (delivered_messages, open_count). 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
Returns three independent analytical reports: (1) a record quality audit flagging missing, null, or out-of-range values per row; (2) a string normalisation report with cleaned/standardized versions of string fields like channel_group; and (3) a trailing mean series showing rolling averages for the selected numeric field (delivered_messages or open_count) over the specified window. All computed locally without external lookups or predictions.
Example use cases
Email campaign data quality check
“I've got 150 rows of newsletter send data with delivered_messages and open_count per channel group and date — can you run a record quality audit and flag any bad or missing values, normalize the channel group strings, and compute a 5-period trailing mean on delivered_messages?”
Rolling open rate trend analysis
“Take my newsletter observation records for the past quarter — each row has a date, channel_group, delivered_messages, and open_count — and give me a trailing mean series with a window of 10 for open_count so I can see the smoothed trend over time.”
Multi-report newsletter data cleanup
“I need three reports on my newsletter dataset: a quality audit to catch bad records, string normalization on channel_group, and a rolling average with window size 4 on delivered_messages — there are about 80 rows total, each identified by row_id.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-news-08-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of record quality auditing, string normalization, and trailing mean computation on newsletter data in a single low-cost call. It is ideal for preprocessing and QA of email campaign datasets before downstream analysis, especially when working with delivered_messages or open_count metrics grouped by channel and date. Prefer this over general-purpose data tools when you want bundled, purpose-built reports for newsletter observation schemas at a fixed $0.01 cost for up to 200 rows.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Numeric values outside ±1,000,000,000,000 range rejected
- Missing required fields (row_id, channel_group, date, delivered_messages, open_count) cause row-level errors
- Window size less than 2 or greater than 200 causes parameter validation failure
- Empty rows array causes request failure
- Payment of 0.01 USDC not provided or rejected causes 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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