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Tutoring Sessions Data Quality Audit (Record QA + String Normalisation + IQR Outlier Flags)
x402
Tutoring Sessions Data Quality Audit (Record QA + 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 2026-10-26).
Audits up to 200 tutoring session records for data quality issues, normalises strings, and flags IQR-based outliers across session_minutes or session_count fields in three independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Tutoring sessions — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied tutoring sessions records (session_minutes, session_count). 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: (1) a record quality audit identifying missing, invalid, or malformed fields; (2) string normalisation results for text fields like course_group; and (3) IQR-based outlier flags on the chosen numeric field (session_minutes or session_count), all computed locally with no external lookups.
Example use cases
Outlier detection in session durations
“I have 80 tutoring session records and I want to know which ones have unusual session_minutes values — can you run an IQR outlier check plus a full record quality audit and string normalisation on them?”
Data quality check before reporting
“Before I submit my tutoring program report, can you audit these 150 session records for data quality problems, normalise the course group names, and flag any outliers in session_count?”
Cleaning messy education data exports
“I exported tutoring session logs from our LMS and some of the course_group strings look inconsistent — can you normalise them, do a record quality audit, and check for IQR outliers in session_minutes across all 200 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-16-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data quality audit, string normalisation, and IQR outlier detection specifically for tutoring or education session records (session_minutes, session_count) in a single call at low cost ($0.01 for up to 200 rows and 3 reports). Prefer it over general-purpose data tools when your dataset fits the education/tutoring schema and you want all three analytical passes without orchestrating separate services.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, date, session_minutes, session_count) cause row-level errors
- Invalid date format (non-YYYY-MM-DD) triggers validation failure
- Null numeric values may be excluded from IQR computation
- field enum value not matching 'session_minutes' or 'session_count' causes schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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