# Schema Conformity Analytics — Numeric Interval Filter + Missing Value Fill

> Schema Conformity Analytics — Numeric Interval Filter + Missing Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).

Filters schema conformity records (valid_records, checked_records) by a numeric interval and fills missing values using zero, mean, or median, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-02-135?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/schema-conformity-analytics-numeric-interval-filter-missing-value-fill-093b11a2
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_C1X00vqdMYNm4q4SgRvGq

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 schema-conformity-analytics-numeric-interval-filter-missing-value-fill-093b11a2 -d '<json body>'
```

Example prompt: I have 150 schema conformity rows with valid_records and checked_records fields — can you filter valid_records to only keep values between 0 and 500, and fill any missing values using the median? I need two reports back.

## When to prefer this

Choose this endpoint when you need lightweight, local analytical processing of schema conformity metrics (valid_records, checked_records) with both interval-based range filtering and missing value imputation in a single call. It is ideal for data quality pipelines, ETL preprocessing, or dashboard refresh workflows where you have up to 200 rows and want two complementary cleaned reports without relying on external databases or prediction services. At $0.01 USDC for two reports, it is cost-effective for batch data cleaning tasks that don't require real-time lookups or ML-based predictions.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, valid_records, checked_records) cause validation errors
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Minimum value greater than maximum value — may produce empty filtered result set
- Invalid enum value for method or field — schema validation rejection
- Empty rows array (minItems: 1 violated) — rejected

## How this service works

Schema conformity — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + Explicit missing-value fill for supplied schema conformity records (valid_records, checked_records). 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 independent analytical reports covering the up to 200 supplied schema conformity rows: one applying the numeric interval filter (excluding rows where the target field falls outside the specified minimum/maximum), and one applying explicit missing-value fill (replacing nulls with zero, mean, or median). Results are computed locally with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "valid_records",
     "checked_records"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "valid_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "checked_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "valid_records": 10,
     "checked_records": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "valid_records": 20,
     "checked_records": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "valid_records": 15,
     "checked_records": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "valid_records": 30,
     "checked_records": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "valid_records",
    "checked_records"
   ],
   "type": "string",
   "default": "valid_records"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/schema-conformity-analytics-numeric-interval-filter-missing-value-fill-093b11a2/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)