# Null Coverage String Normalisation + IQR Outlier Flags

> Null Coverage String Normalisation + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).

Normalises string fields and flags IQR-based outliers in null coverage records (missing_values, total_values) for up to 200 rows, returning 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-03-032?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/null-coverage-string-normalisation-iqr-outlier-flags-68a78358
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_wu_2YNO0HjBAAUiMweGun

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 null-coverage-string-normalisation-iqr-outlier-flags-68a78358 -d '<json body>'
```

Example prompt: Can you run string normalisation and IQR outlier detection on these null coverage records? I have 4 rows: row-1 (group-a, 2026-10-01, missing=10, total=1), row-2 (group-b, 2026-10-02, missing=20, total=2), row-3 (group-a, 2026-10-03, missing=15, total=3), row-4 (group-b, 2026-10-04, missing=30, total=4) — flag outliers on the missing_values field.

## When to prefer this

Choose this endpoint when you need both string normalisation and IQR-based outlier detection on null coverage data (missing_values and total_values) in a single cheap call. It is ideal for data quality pipelines, ETL monitoring, and completeness auditing where you want statistical outlier flags without external lookups or ML predictions. Prefer it over generic stats APIs when your data is structured as dataset_group/date/missing_values/total_values records and you need two bundled local reports at low cost.

## Known failure modes

- Missing required fields (row_id, date, missing_values, total_values) causes validation error
- More than 200 rows submitted exceeds the maxItems limit and is rejected
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Null or out-of-range numeric values may affect IQR computation accuracy
- Invalid field enum value (not 'missing_values' or 'total_values') causes rejection
- Empty rows array (minItems:1 violated) returns an error

## How this service works

Null coverage — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied null coverage records (missing_values, total_values). 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 analytical reports per call: (1) a string normalisation report for the dataset_group and related string fields, and (2) an IQR outlier flag report identifying which rows fall outside the interquartile range for the selected numeric field (missing_values or total_values). Covers up to 200 input rows with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/null-coverage-string-normalisation-iqr-outlier-flags-68a78358/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)