# Transformation Throughput Analytics — Numeric Interval Filter + Explicit Missing-Value Fill

> Transformation Throughput Analytics — Numeric Interval Filter + Explicit 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 2026-10-15).

Filters transformation throughput records by a numeric interval and fills missing values in processed_rows or duration_seconds fields using zero, mean, or median imputation, returning up to 2 analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-135?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/transformation-throughput-analytics-numeric-interval-filter-explicit-c0c1cf86
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Qh6zQUEc58DzCgm2X6uYJ

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 transformation-throughput-analytics-numeric-interval-filter-explicit-c0c1cf86 -d '<json body>'
```

Example prompt: I have 150 transformation throughput records with some missing processed_rows values — can you filter them to keep only rows where processed_rows is between 0 and 100, and fill any missing processed_rows values using the median? I need 2 reports back.

## When to prefer this

Choose this endpoint when you have transformation throughput records (with processed_rows and duration_seconds metrics) and need to simultaneously apply a numeric range filter AND handle missing values in a single analytical pass, receiving up to 2 structured reports. It is ideal for data engineering teams cleaning ETL job metrics without needing external data sources, predictions, or live lookups. Prefer it over general-purpose data tools when your dataset fits within 200 rows and you want a cost-effective ($0.01 USDC) local computation.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected
- Invalid date format (not YYYY-MM-DD) in any row — validation error
- minimum greater than maximum — invalid interval configuration
- Unknown fill method value outside zero/mean/median enum — schema error
- Missing required fields (row_id, date, processed_rows, duration_seconds) in any row — validation failure
- Empty rows array — rejected with minItems violation

## How this service works

Transformation throughput — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + Explicit missing-value fill for supplied transformation throughput records (processed_rows, duration_seconds). 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 up to 2 analytical reports covering the supplied transformation throughput rows after applying the specified numeric interval filter (keeping rows where the target field falls within [minimum, maximum]) and filling any null values in the target field using the chosen method (zero, mean, or median). Reports are generated locally without external lookups or predictions, covering up to 200 input rows per call.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "processed_rows",
     "duration_seconds"
    ],
    "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
     },
     "processed_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "duration_seconds": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "processed_rows": 10,
     "duration_seconds": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "processed_rows": 20,
     "duration_seconds": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "processed_rows": 15,
     "duration_seconds": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "processed_rows": 30,
     "duration_seconds": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "processed_rows",
    "duration_seconds"
   ],
   "type": "string",
   "default": "processed_rows"
  },
  "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/transformation-throughput-analytics-numeric-interval-filter-explicit-c0c1cf86/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)