# Numeric Precision Comparisons — Record Quality Audit & Exact Row Deduplication

> Numeric Precision Comparisons — Record Quality Audit & Exact Row Deduplication 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).

Audits data quality and deduplicates rows for up to 200 numeric source/transformed value pairs, returning two independent analytical reports offline.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-002?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-exact-row-4406f9fa
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_lO5BV9stiBoPZUm17xT9L

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-exact-row-4406f9fa -d '<json body>'
```

Example prompt: I have a batch of up to 200 rows from our ETL pipeline with row IDs, dataset groups, dates, source values, and transformed values — can you run a record quality audit and exact row deduplication check on them and give me both reports?

## When to prefer this

Choose this endpoint when you need both a data quality audit and exact row deduplication in a single call on a batch of up to 200 rows with numeric source and transformed values. Ideal for ETL pipeline validation, pre-load QA checks, or verifying numeric precision after transformation steps. Best when you have structured row data with identifiable groups and dates and need two independent offline reports without any external API calls or predictions.

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, date, source_value, transformed_value, dataset_group) — validation error
- Date fields not matching YYYY-MM-DD pattern — schema validation failure
- Numeric values outside ±1,000,000,000,000 range — schema rejection
- Empty rows array — minItems constraint violation
- Payment of 0.01 USDC not completed — 402 response blocking access

## How this service works

Numeric precision comparisons — Record quality audit + Exact row deduplication: Record quality audit + Exact row deduplication for supplied numeric precision comparisons records (source_value, transformed_value). 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 independent reports: (1) a record quality audit flagging issues such as null values, out-of-range numbers, or precision anomalies in source_value and transformed_value columns; and (2) an exact row deduplication report identifying which rows are exact duplicates based on all supplied fields. Both reports cover up to 200 rows and are generated locally without 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-exact-row-4406f9fa/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)