# Numeric Precision Comparisons — Record Quality Audit + Explicit Missing-Value Fill

> Numeric Precision Comparisons — Record Quality Audit + Explicit Missing-Value Fill 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 fills missing values for up to 200 rows of numeric source/transformed value pairs, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-019?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-explicit-missing-427ad7c1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_5Nb_GhoTUWUesn5QM3SWM

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-explicit-missing-427ad7c1 -d '<json body>'
```

Example prompt: Can you run a record quality audit and fill any missing values using median imputation on these 4 rows of source/transformed pairs — row-1 group-a 2026-10-01 source=10 transformed=null, row-2 group-b 2026-10-02 source=null transformed=2 — and apply the fill to the source_value field?

## When to prefer this

Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) dual-report quality check on paired numeric source/transformed data — especially when you want both a quality audit AND null imputation in one call without any external API dependencies. Prefer this over generic data validation tools when your use case specifically involves comparing source vs. transformed numeric values by row and group, and you need a controlled fill strategy (zero, mean, or median).

## Known failure modes

- Rows array is empty or missing — returns validation error
- More than 200 rows submitted — request rejected with maxItems error
- row_id missing or empty string — schema validation failure
- Date field does not match YYYY-MM-DD pattern — rejected
- Numeric values outside ±1,000,000,000,000 range — rejected
- Invalid fill method or field enum value — schema error

## How this service works

Numeric precision comparisons — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill 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

Returns two independent analytical reports: (1) a record quality audit summarizing completeness, null counts, and data integrity issues across the submitted rows; and (2) an explicit missing-value fill report showing which rows had nulls replaced using the selected fill method (zero, mean, or median) for the specified field. Both reports operate 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-record-quality-audit-explicit-missing-427ad7c1/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)