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Account Research Coverage — Adjacent Row Changes + IQR Outlier Flags
x402
Account Research Coverage — Adjacent Row Changes + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Analyzes up to 200 account research coverage records by computing adjacent row changes and flagging IQR-based outliers for researched_accounts or target_accounts metrics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Account research coverage — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied account research coverage records (researched_accounts, target_accounts). 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) adjacent row change analysis showing delta values between consecutive rows for the selected field, and (2) IQR-based outlier flags identifying rows where the metric falls outside the interquartile range boundaries — all computed locally on the supplied data with no external lookups.
Example use cases
Spot anomalies in weekly account research
“I have 90 rows of weekly account research coverage data across two company groups — can you check the researched_accounts field for sudden row-over-row jumps and flag any IQR outliers?”
Audit target account counts for data quality
“Before I finalize my quarterly report, can you run adjacent row change detection and IQR outlier flags on our target_accounts field across these 150 records grouped by company?”
Identify coverage spikes by group
“We've got about 60 rows of account coverage data broken out by company group and date — can you analyze target_accounts for unusual adjacent changes and statistical outliers using IQR?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-08-157?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, self-contained statistical anomaly detection on account research coverage data without any external API calls or live data enrichment. It is ideal for data quality audits, pre-reporting checks, or pipeline validation steps where you supply the rows and need both row-over-row change signals and IQR outlier flags in a single low-cost call. Prefer alternatives if you need live CRM data fetching, predictive modeling, or analysis on more than 200 rows.
Side effects
- Missing required fields (row_id, date, company_group, researched_accounts, target_accounts) cause validation errors
- Submitting more than 200 rows exceeds the row limit and will be rejected
- Invalid date format (non YYYY-MM-DD) triggers schema validation failure
- Selecting an invalid field value outside the enum causes a request error
- Null values in the selected numeric field may affect IQR and delta calculations
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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