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Savings Ledger Analytics — Record Quality Audit + String Normalisation + Two-Measure Correlation
x402
Savings Ledger Analytics — Record Quality Audit + String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Runs three bundled analytical reports on savings ledger rows (deposit_amount, withdrawal_amount): record quality audit, string normalisation, and two-measure correlation — all locally, no external calls.
Price / call$0.01
Success—
StatusUnknown
How this service works
Savings ledger — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation for supplied savings ledger records (deposit_amount, withdrawal_amount). 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 structured analytical reports returned together: (1) a record quality audit flagging missing, null, or out-of-range values per row; (2) a string normalisation report showing standardised versions of text fields like instrument_group; (3) a two-measure correlation report showing the statistical relationship between deposit_amount and withdrawal_amount across all submitted rows.
Example use cases
Monthly savings data quality check
“I have 50 rows of savings ledger data for January — each has a row ID, date, instrument group, deposit amount, and withdrawal amount. Can you audit the record quality, normalise the instrument group strings, and tell me the correlation between deposits and withdrawals?”
Pre-import ledger validation
“Before I import these 120 savings ledger rows into our system, can you check them for data quality issues, fix any inconsistent string formatting in the instrument groups, and show me how correlated the deposit and withdrawal figures are?”
Savings portfolio anomaly investigation
“I've got 30 rows of savings transactions across group-a and group-b from Q3 — can you run a quality audit, normalise the strings, and compute the two-measure correlation between deposit_amount and withdrawal_amount to help me spot anomalies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-23-204?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three analytical reports (quality audit, string normalisation, and two-measure correlation) on savings ledger data in a single low-cost call. Ideal for validating deposit/withdrawal datasets of up to 200 rows before downstream processing. Prefer this over separate analytics tools when you want bundled, offline, local computation with no external data access or prediction — purely descriptive analytics on your supplied rows.
Side effects
- Fewer than 1 row submitted — request rejected with validation error
- More than 200 rows submitted — exceeds maxItems limit, request rejected
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, date, deposit_amount, withdrawal_amount, instrument_group) — validation error
- Payment not included or insufficient — 402 Payment Required response
- Amount values outside ±1,000,000,000,000 range — schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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