# Validation Rule Results — String Normalisation + Two-Measure Correlation

> Validation Rule Results — String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-11).

Analyzes up to 200 validation rule result records by normalizing string fields and computing two-measure correlation between passed_rules and total_rules, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-08-033?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-11
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/validation-rule-results-string-normalisation-two-measure-correlation-214cd92e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_exii1DS16V69ZmOfw34dq

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 validation-rule-results-string-normalisation-two-measure-correlation-214cd92e -d '<json body>'
```

Example prompt: I have validation rule results for two dataset groups — 'group-a' and 'group-b' — covering dates from 2026-10-01 to 2026-10-04, with passed_rules and total_rules values for each row. Can you normalize the string fields and compute the correlation between passed_rules and total_rules across all rows, returning both reports?

## When to prefer this

Choose this endpoint when you need both string field normalization and a two-measure correlation analysis on validation rule result records in a single call. Prefer it over sibling endpoints when your primary interest is the statistical relationship between passed_rules and total_rules alongside string cleaning, and you do not need outlier flagging, deduplication, or daily sum series — those are covered by other specialized endpoints in the same family.

## Known failure modes

- Fewer than 1 row submitted — minimum of 1 row required
- More than 200 rows submitted — exceeds maximum batch size
- Missing required fields (row_id, dataset_group, date, passed_rules, total_rules) on any row — validation error
- Date fields not matching YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Null values for both passed_rules and total_rules across all rows may degrade correlation computation

## How this service works

Validation rule results — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied validation rule results records (passed_rules, total_rules). 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 string normalization report cleaning and standardizing the text fields (row_id, dataset_group, date) across the submitted rows, and (2) a two-measure correlation report quantifying the statistical relationship between passed_rules and total_rules across all submitted records. No external lookups or predictions are included.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "passed_rules",
     "total_rules"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "total_rules": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "passed_rules": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "total_rules": 1,
     "passed_rules": 10,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "total_rules": 2,
     "passed_rules": 20,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "total_rules": 3,
     "passed_rules": 15,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "total_rules": 4,
     "passed_rules": 30,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/validation-rule-results-string-normalisation-two-measure-correlation-214cd92e/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)