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Account Research Coverage Histogram & IQR Outlier Flags
x402
Account Research Coverage Histogram & 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).
Computes equal-width histograms and IQR-based outlier flags for account research coverage data (researched_accounts and target_accounts fields) across up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Account research coverage — Equal-width histogram + IQR outlier flags: Equal-width histogram + 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 reports sharing local analytical engines: (1) an equal-width histogram dividing the chosen numeric field into the specified number of bins with counts per bin, and (2) IQR-based outlier flags identifying which row IDs fall outside the interquartile fence. No external data is fetched; all analysis is performed on the supplied rows only.
Example use cases
Sales team coverage gap detection
“I have a dataset of 120 rows tracking how many accounts our sales reps researched vs targeted each day by company group. Can you run an equal-width histogram with 10 bins and flag any IQR outliers on the researched_accounts field so I can see where coverage is unusually high or low?”
Target account pipeline anomaly check
“We track target_accounts daily across several company groups and I suspect some entries are way off. Can you build a histogram with 5 bins and apply IQR outlier detection on the target_accounts field across our 80 records so we can spot the anomalies?”
Multi-group research activity distribution review
“I want to understand how our account research activity is distributed across group-a and group-b over the past quarter — I have 150 rows with researched_accounts and target_accounts. Run a 6-bin histogram with IQR outlier flags on the researched_accounts field and tell me which rows look unusual.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-08-170?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of account research coverage data and need both a distribution histogram and IQR-based outlier detection in a single low-cost call. It is ideal for sales ops or revenue teams auditing account research activity without needing external data enrichment, live lookups, or predictive modeling. Prefer it over general-purpose analytics tools when the specific fields researched_accounts and target_accounts are your primary metrics of interest.
Side effects
- Fewer than 1 row supplied returns a validation error
- More than 200 rows submitted exceeds the row limit and is rejected
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Null values in the selected field may reduce effective sample size for IQR calculation
- bins value outside 1–30 range is rejected
- Missing required fields (row_id, company_group, date) cause row-level errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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