# Numeric Precision Comparisons — Record Quality Audit & Adjacent Row Changes

> Numeric Precision Comparisons — Record Quality Audit & 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).

Runs two analytical reports (record quality audit and adjacent row changes) on up to 200 rows of numeric precision comparison data (source_value vs transformed_value), using local analytical engines with no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-011?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-adjacent-row-changes-a99c1c2c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_KoyG1aOjRVRbr2njpnbfn

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-adjacent-row-changes-a99c1c2c -d '<json body>'
```

Example prompt: Run a record quality audit and adjacent row changes analysis on my transformation data — I have 4 rows in groups 'group-a' and 'group-b' dated 2026-10-01 through 2026-10-04, with source values [10, 20, 15, 30] and transformed values [1, 2, 3, 4], and I want to analyze the source_value field.

## When to prefer this

Choose this endpoint when you need two bundled analytical reports — record quality audit plus adjacent row changes — on a batch of up to 200 rows containing paired source/transformed numeric values, and you want local computation with no external data dependencies. It is cost-effective at $0.01 USDC for both reports simultaneously, making it ideal for ETL validation, data pipeline QA, or precision drift checks where you already have the data in hand.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, source_value, transformed_value) causes rejection
- Date strings not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array (minItems: 1 violated) returns an error
- Payment of 0.01 USDC required per call; unpaid requests are rejected

## How this service works

Numeric precision comparisons — Record quality audit + Adjacent row changes: Record quality audit + Adjacent row changes 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 analytical reports: (1) a record quality audit highlighting data quality issues across the supplied rows (e.g. nulls, outliers, precision anomalies), and (2) an adjacent row changes report showing how values shift between consecutive rows — all computed locally on the supplied 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-adjacent-row-changes-a99c1c2c/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)