# Numeric Precision Comparisons — Record Quality Audit + String Normalisation + Date by Category Pivot

> Numeric Precision Comparisons — Record Quality Audit + String Normalisation + Date by Category Pivot 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).

Runs three local analytical reports (record quality audit, string normalisation, and date-by-category pivot) on up to 200 rows of numeric precision comparison data containing source and transformed values.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-206?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/numeric-precision-comparisons-record-quality-audit-string-f2da1c91
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_weU9NOISZrXSkNp_sR91p

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability numeric-precision-comparisons-record-quality-audit-string-f2da1c91 -d '<json body>'
```

Example prompt: Can you run the three-report bundle — record quality audit, string normalisation, and date-by-category pivot — on my numeric precision comparison rows? Here are up to 50 rows each with a row_id, dataset_group like 'group-a', a date in YYYY-MM-DD format, a source_value, and a transformed_value; use source_value as the field to focus on.

## When to prefer this

Choose this endpoint when you need three bundled analytical reports — record quality audit, string normalisation, and date-by-category pivot — on a batch of up to 200 numeric precision comparison rows in a single low-cost call. It is ideal for ETL validation, data quality checks before ingestion, or post-transformation audits where source and transformed values need to be compared. Prefer it over single-report endpoints when all three report types are relevant and cost efficiency matters. Not suitable when you need live data lookup, predictions, or external integrations.

## Known failure modes

- Empty or missing rows array returns a validation error
- More than 200 rows submitted — exceeds maxItems limit
- Row missing required fields (row_id, dataset_group, date, source_value, transformed_value) causes schema rejection
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside ±1,000,000,000,000 range are rejected
- Invalid field enum value (not 'source_value' or 'transformed_value') causes rejection
- Payment of 0.01 USDC not settled — x402 payment required error

## How this service works

Numeric precision comparisons — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied numeric precision comparisons records (source_value, transformed_value). 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 computed locally on the supplied rows: (1) a record quality audit flagging completeness and anomalies, (2) a string normalisation report standardising text fields, and (3) a date-by-category pivot summarising numeric values grouped by date and dataset_group. All analysis is performed on the submitted data with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "source_value",
     "transformed_value"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "transformed_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_value": 10,
     "dataset_group": "group-a",
     "transformed_value": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_value": 20,
     "dataset_group": "group-b",
     "transformed_value": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_value": 15,
     "dataset_group": "group-a",
     "transformed_value": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_value": 30,
     "dataset_group": "group-b",
     "transformed_value": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "source_value",
    "transformed_value"
   ],
   "type": "string",
   "default": "source_value"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-record-quality-audit-string-f2da1c91/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)