# Numeric Precision Comparisons — Record Quality Audit + Exact Row Deduplication + Adjacent Row Changes

> Numeric Precision Comparisons — Record Quality Audit + Exact Row Deduplication + Adjacent Row Changes 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).

Analyzes up to 200 rows of source_value/transformed_value numeric pairs, producing three independent local reports: a record quality audit, exact row deduplication, and adjacent row change detection.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-217?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-74a1e954
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_fbQViaWdawbLWSxBLe_yf

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-74a1e954 -d '<json body>'
```

Example prompt: Can you run the three-report numeric precision audit on my dataset — checking record quality, exact duplicates, and adjacent row changes — for these rows using the source_value field? Here's the data: row-1, group-a, 2026-10-01, source=10, transformed=1; row-2, group-b, 2026-10-02, source=20, transformed=2.

## When to prefer this

Choose this endpoint when you need all three data quality operations — record audit, deduplication, and adjacent change detection — in a single call for up to 200 rows of numeric source/transformed value pairs. It is ideal for ETL validation, data pipeline QA, and numeric transformation auditing where no external data lookup or ML prediction is needed and cost efficiency matters ($0.01 for three reports).

## Known failure modes

- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, dataset_group, date, source_value, transformed_value) cause validation errors
- Date strings not matching YYYY-MM-DD pattern are rejected
- Numeric values outside [-1,000,000,000,000, 1,000,000,000,000] range are rejected
- Empty rows array returns an error
- Invalid field enum value (must be source_value or transformed_value) causes rejection

## How this service works

Numeric precision comparisons — Record quality audit + Exact row deduplication + Adjacent row changes: Record quality audit + Exact row deduplication + Adjacent row changes 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 for the submitted rows: (1) a record quality audit flagging nulls, out-of-range values, and completeness issues; (2) an exact row deduplication report identifying identical records; and (3) an adjacent row change report showing value differences between consecutive rows — all computed 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
  },
  "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-exact-row-74a1e954/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)