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Onboarding Progress — String Normalisation + IQR Outlier Flags
x402
Onboarding Progress — String Normalisation + 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 2026-11-04).
Normalises string fields and flags IQR-based outliers in onboarding progress records (completed_steps, required_steps) for up to 200 rows, producing 2 analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Onboarding progress — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied onboarding progress records (completed_steps, required_steps). 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
Two analytical reports per call: one covering string normalisation results for the supplied onboarding rows, and one flagging rows whose completed_steps or required_steps values fall outside the interquartile range (IQR) bounds, indicating statistical outliers. Reports are based entirely on the supplied data with no external lookups.
Example use cases
Audit onboarding completion anomalies
“I have 150 rows of onboarding progress records with completed_steps and required_steps for various team groups — can you normalise the string columns and flag any IQR outliers in the completed_steps field so I can see which employees have unusually high or low completion counts?”
Clean team group strings for reporting
“Our onboarding dataset for Q4 has inconsistent team group labels and messy step counts across about 80 rows — run string normalisation and IQR outlier detection on the required_steps field so I can clean this up before sending it to leadership.”
Detect data entry errors in step records
“I suspect some of our onboarding records have data entry mistakes in the completed_steps numbers — can you take these 50 rows with dates and team groups and run the IQR outlier flagging plus string normalisation to surface anything that looks statistically off?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-operations-08-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have onboarding progress tabular data (up to 200 rows) and need both string normalisation and IQR-based outlier detection in a single low-cost call. It is ideal for lightweight data quality audits on completed_steps or required_steps without needing a full analytics platform. Prefer alternatives if you need predictive modelling, live data lookup, or datasets exceeding 200 rows.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, team_group, completed_steps, required_steps) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Field selector not one of 'completed_steps' or 'required_steps' — enum validation error
- Too few rows to compute meaningful IQR (e.g. single row) — may return degenerate or empty outlier flags
- Payment not provided or insufficient USDC — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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