# Numeric Precision Comparisons — IQR Outlier Flags + Date by Category Pivot

> Numeric Precision Comparisons — IQR Outlier Flags + Date by Category Pivot 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).

Runs two local analytical reports on supplied numeric precision comparison records: IQR-based outlier flagging and a date-by-category pivot table, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-178?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-iqr-outlier-flags-date-by-category-pivot-9e16a06e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1NzPtnIUTn-n9K7SV_9BR

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-iqr-outlier-flags-date-by-category-pivot-9e16a06e -d '<json body>'
```

Example prompt: I have 150 rows of source-value vs transformed-value comparison records across groups like 'group-a' and 'group-b' with dates — can you flag the IQR outliers and also pivot the data by date and category for source_value?

## When to prefer this

Choose this endpoint when you need both IQR outlier detection and a date-by-category pivot in a single low-cost call on a batch of up to 200 numeric comparison records (source vs transformed values). It is ideal for ETL validation, data quality audits, and transformation pipeline reviews where you need statistical anomaly flags alongside a temporal-categorical summary. It does not call external APIs or make predictions — all analysis is local and deterministic.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, source_value, transformed_value) cause validation errors
- Date strings not matching YYYY-MM-DD pattern will fail schema validation
- Numeric values outside ±1,000,000,000,000 range will be rejected
- All-null values for the selected field may produce empty or degenerate IQR results
- Insufficient row count for meaningful IQR computation may yield trivial outlier flags

## How this service works

Numeric precision comparisons — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot 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 analytical reports: (1) IQR outlier flags identifying which rows have values outside the interquartile range for the selected field, and (2) a date-by-category pivot table aggregating the numeric comparison data by date and dataset_group — all computed locally from the supplied rows with no external data access.

## 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-iqr-outlier-flags-date-by-category-pivot-9e16a06e/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)