# Dataset Null Pattern Audit

> Dataset Null Pattern Audit is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.010063/call, status unknown (last checked 2026-10-19).

Counts missing fields, nulls, empty strings, and populated cells per column across a supplied dataset, without imputation or PII detection.

## Facts

- Endpoint: POST https://www.mahastrategies.com/api/v1/verify/datasets/null-patterns?utm_source=zero.xyz
- Price: $0.010063/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-null-pattern-audit-5b45b612
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap__mq1cgRo3FjVT1C3e41vH

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 dataset-null-pattern-audit-5b45b612 -d '<json body>'
```

Example prompt: Audit this public dataset for null patterns — I have 200 rows and 10 columns named ['id','name','age','email','city','country','score','grade','timestamp','status']. Give me a breakdown of missing fields, nulls, empty strings, and populated cells per column.

## When to prefer this

Choose this endpoint when you need a fast, lightweight null-pattern audit of a tabular dataset (up to 500 rows, 64 columns) and only want raw counts of missing, null, empty, and populated cells — without any data imputation, transformation, or PII/sensitive-data detection. It is ideal as a pre-processing quality gate or reporting step in data pipelines where auditability and simplicity matter more than remediation.

## Known failure modes

- Exceeds 500-row limit — request rejected with a validation error
- Column list is empty or exceeds 64 columns — schema validation failure
- Invalid dataClass value (not 'synthetic' or 'public') — enum validation error
- Malformed row objects that don't match declared columns — partial or error response
- Network timeout for large payloads near the row/column limits

## How this service works

Count missing fields, nulls, empty strings and populated cells separately. No imputation or private-data detection.

## Output

Returns per-column counts of missing fields, null values, empty strings, and populated cells across the submitted dataset rows, enabling a clear picture of data completeness without any imputation or sensitive-data processing.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "maxItems": 500
  },
  "columns": {
   "type": "array",
   "maxItems": 64,
   "minItems": 1,
   "uniqueItems": true
  },
  "dataClass": {
   "enum": [
    "synthetic",
    "public"
   ],
   "type": "string"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-null-pattern-audit-5b45b612/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from www.mahastrategies.com](https://www.zero.xyz/host/www.mahastrategies.com/llms.txt)