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Feed Observations — Record Quality Audit + String Normalisation + Numeric Interval Filter
x402
Feed Observations — Record Quality Audit + 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 2026-11-03).
Audits record quality, normalises strings, and filters by numeric interval for up to 200 feed observation rows in a single call, producing 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feed observations — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter for supplied feed observations records (item_count, duplicate_items). 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 for the supplied feed observation rows: (1) a record quality audit identifying missing, malformed, or invalid fields; (2) a string normalisation report for the site_group field; and (3) a numeric interval filter report showing which rows have the selected field (item_count or duplicate_items) within the specified minimum/maximum range. All processing is local with no external lookups.
Example use cases
Feed data quality check before ingestion
“I have a batch of feed observation rows with item_count and duplicate_items — can you audit their record quality, normalise the site_group strings, and flag any rows where item_count falls outside 5 to 500?”
Duplicate rate anomaly investigation
“Run a quality audit and numeric interval filter on my feed observations, filtering on duplicate_items between 0 and 10, so I can spot rows with suspiciously high duplicate counts before I process them.”
Pre-reporting data cleaning for site groups
“Before I build my weekly report, can you normalise the site_group strings and do a record quality audit on these 120 feed observation rows, and also filter item_count between 1 and 1000 to catch any outliers?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-11-197?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined record quality audit, string normalisation, and numeric interval filter for feed observation data (item_count, duplicate_items) in a single low-cost call. It is ideal for pre-ingestion data validation pipelines, ad-hoc data cleaning, or quality checks on batches up to 200 rows. Prefer it over general-purpose data tools when you specifically need all three reports together and your data conforms to the feed observations schema.
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — request rejected
- Invalid date format (non-YYYY-MM-DD) in any row — validation error
- Missing required row fields (row_id, site_group, date, item_count, duplicate_items) — schema error
- Numeric values outside allowed range (±1,000,000,000,000) — schema rejection
- Invalid field enum value (must be item_count or duplicate_items) — parameter error
- minimum greater than maximum — may produce empty interval filter result
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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