# Numeric Precision Comparisons — Record Quality Audit + String Normalisation + Category Frequencies

> Numeric Precision Comparisons — Record Quality Audit + String Normalisation + Category Frequencies 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 category frequencies) on up to 200 rows of numeric source/transformed value pairs grouped by dataset category and date.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-195?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-5999c0cb
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Xu0sbMMeNLCp9ZmvSP42K

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-5999c0cb -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and category frequency report on these 4 rows I have — row-1 in group-a on 2026-10-01 with source 10 and transformed 1, row-2 in group-b on 2026-10-02 with source 20 and transformed 2, row-3 in group-a on 2026-10-03 with source 15 and transformed 3, and row-4 in group-b on 2026-10-04 with source 30 and transformed 4?

## When to prefer this

Choose this endpoint when you need three bundled local data-quality reports (quality audit, string normalisation, category frequencies) on numeric source/transformed value pairs in a single low-cost call. It is ideal for ETL validation, post-migration checks, and data profiling workflows where you have up to 200 rows and want offline analysis without external API dependencies. Prefer alternatives if you need live data lookup, predictive modelling, or more than 200 rows processed at once.

## Known failure modes

- Exceeding 200 rows returns a validation error — trim input to 200 rows maximum
- Missing required fields (row_id, dataset_group, date, source_value, transformed_value) on any row causes rejection
- Invalid date format (not YYYY-MM-DD) triggers a schema validation failure
- Numeric values outside the ±1,000,000,000,000 range are rejected
- Empty rows array returns an error — at least 1 row is required

## How this service works

Numeric precision comparisons — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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

Returns three independent analytical reports covering: (1) a record quality audit flagging nulls, out-of-range values, and row-level anomalies; (2) a string normalisation report highlighting formatting inconsistencies in numeric representations; and (3) a category frequency summary showing how often each dataset_group appears across the supplied rows. All analysis is performed locally 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-record-quality-audit-string-5999c0cb/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)