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Validation Rule Results — Duplicate Key Report + Trailing Mean Series
x402
Validation Rule Results — Duplicate Key Report + 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 2026-10-14).
Analyzes validation rule result records to detect duplicate keys and compute a trailing mean time series for passed_rules or total_rules metrics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Validation rule results — Duplicate key report + Trailing mean series: Duplicate key report + Trailing mean series for supplied validation rule results records (passed_rules, total_rules). Up to 200 …
Output
Returns a duplicate key report identifying any row_id values that appear more than once in the input, plus a trailing mean time series for the selected field (passed_rules or total_rules) computed over the specified rolling window across the submitted records.
Example use cases
Monitor data pipeline validation health
“I have daily validation rule results from two dataset groups — can you flag any duplicate row IDs and show me a trailing mean of passed_rules with a window of 5 so I can spot quality regressions over time?”
Audit rule compliance for a regulatory report
“Run a duplicate key check and compute a 7-day trailing mean of total_rules for my validation records across group-a and group-b, covering the last two weeks of October 2026.”
Detect anomalies in ETL validation output
“My ETL job produces validation rule result rows daily — can you scan them for duplicate keys and give me a smoothed trailing mean of passed_rules with window size 4 to help me spot anomalies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-08-061?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously detect duplicate key issues and compute smoothed rolling averages in validation rule result datasets. It is purpose-built for data quality monitoring workflows where both data integrity (duplicates) and trend analysis (trailing mean) on rule pass/fail counts are needed together. Prefer it over generic analytics endpoints when your data follows the row_id + dataset_group + date + passed_rules + total_rules schema.
Side effects
- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — exceeds maxItems limit
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Window size below 2 or above 200 — rejected input
- Unknown field value outside enum ['passed_rules','total_rules'] — schema error
- Null numeric values may affect trailing mean computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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