# Validation Rule Results Analyzer — Record Quality Audit, String Normalisation & Base-100 Series

> Validation Rule Results Analyzer — Record Quality Audit, String Normalisation & Base-100 Series 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).

Runs three local analytical reports (record quality audit, string normalisation, and base-100 index series) on supplied validation rule result records (passed_rules, total_rules) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-08-201?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-analyzer-record-quality-audit-string-c3095a5b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ULKh8XuEBOOn3MoOQeyWB

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-analyzer-record-quality-audit-string-c3095a5b -d '<json body>'
```

Example prompt: Can you run the record quality audit, string normalisation, and base-100 series reports on my validation rule results? I have 4 rows: row-1 on 2026-10-01 with passed_rules=10 and total_rules=1 in group-a, row-2 on 2026-10-02 with passed_rules=20 total_rules=2 in group-b, row-3 on 2026-10-03 passed_rules=15 total_rules=3 in group-a, row-4 on 2026-10-04 passed_rules=30 total_rules=4 in group-b — use passed_rules as the field and start the base-100 index from row 0.

## When to prefer this

Choose this endpoint when you need three bundled analytical views — quality audit, string normalisation, and base-100 indexing — on validation rule result data (passed_rules, total_rules) in a single low-cost call. It is best suited for offline/batch analysis of up to 200 rows with no need for external lookups, live data, or predictions. Prefer alternatives if you need real-time validation against a live schema, ML-based anomaly detection, or more than 200 rows processed at once.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — rejected with validation error
- Missing required fields (row_id, date, dataset_group, passed_rules, total_rules) in any row
- Date strings not matching YYYY-MM-DD pattern — rejected by schema validation
- base_index value out of range (must be 0–199 and within actual row count)
- Null values in passed_rules or total_rules may affect numeric index computation
- Payment not included or insufficient — x402 payment required at $0.01 USDC per call

## How this service works

Validation rule results — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series for supplied validation rule results records (passed_rules, total_rules). 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 supplied rows: (1) a record quality audit flagging completeness and consistency issues per row, (2) a string normalisation report cleaning and standardising text fields like row_id and dataset_group, and (3) a base-100 index series for the selected numeric field (passed_rules or total_rules) rebased to 100 at the specified row. All computation is local with no external lookups.

## 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
  },
  "field": {
   "enum": [
    "passed_rules",
    "total_rules"
   ],
   "type": "string",
   "default": "passed_rules"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/validation-rule-results-analyzer-record-quality-audit-string-c3095a5b/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)