# Pipeline Durations Analytics: Numeric Interval Filter + Date-by-Category Pivot

> Pipeline Durations Analytics: Numeric Interval Filter + 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 2026-10-16).

Runs two local analytical reports on pipeline duration data — a numeric interval filter and a date-by-category pivot — for up to 200 supplied rows of run_seconds and processed_rows data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-05-133?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/pipeline-durations-analytics-numeric-interval-filter-date-by-category-946bc476
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_9ZBcjrquAAXx9ec7RDNmh

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 pipeline-durations-analytics-numeric-interval-filter-date-by-category-946bc476 -d '<json body>'
```

Example prompt: I have pipeline run data for up to 200 rows — can you filter the records where run_seconds is between 10 and 500, and also pivot the run_seconds values by date and dataset_group, so I can see which groups are slow on which days?

## When to prefer this

Choose this endpoint when you have already collected pipeline duration records (up to 200 rows) and need two quick, offline analytical views: a numeric range filter and a date-by-category pivot — without needing any live data lookup or external enrichment. It is well-suited for ETL monitoring, SLA auditing, or batch job performance review where the data is already on hand and you want structured summaries at minimal cost ($0.01 USDC for both reports).

## Known failure modes

- Input exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, run_seconds, processed_rows) cause validation errors
- Date values not matching YYYY-MM-DD pattern are rejected
- Minimum greater than maximum produces empty interval filter results
- Null values in the selected numeric field may be excluded from interval filtering
- Invalid field enum value (not 'run_seconds' or 'processed_rows') causes a schema validation error

## How this service works

Pipeline durations — Numeric interval filter + Date by category pivot: Numeric interval filter + Date by category pivot for supplied pipeline durations records (run_seconds, processed_rows). 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 derived from the supplied pipeline duration records: (1) rows filtered to only those where the selected field (run_seconds or processed_rows) falls within the specified numeric minimum-maximum interval, and (2) a pivot table crossing date against dataset_group, aggregating the selected metric — all computed locally from supplied data with no external lookups, covering up to 200 input rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "run_seconds",
     "processed_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "run_seconds": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "processed_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "run_seconds": 10,
     "dataset_group": "group-a",
     "processed_rows": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "run_seconds": 20,
     "dataset_group": "group-b",
     "processed_rows": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "run_seconds": 15,
     "dataset_group": "group-a",
     "processed_rows": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "run_seconds": 30,
     "dataset_group": "group-b",
     "processed_rows": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "run_seconds",
    "processed_rows"
   ],
   "type": "string",
   "default": "run_seconds"
  },
  "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/pipeline-durations-analytics-numeric-interval-filter-date-by-category-946bc476/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)