# Pipeline Durations Analytics: Record Quality Audit + String Normalisation + Date by Category Pivot

> Pipeline Durations Analytics: Record Quality Audit + String Normalisation + 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 2026-10-18).

Runs three analytical reports (record quality audit, string normalisation, and date-by-category pivot) on supplied pipeline duration records containing run_seconds and processed_rows fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-05-206?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/pipeline-durations-analytics-record-quality-audit-string-68695640
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_vvzzdKOzJ4treViMerX4k

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-record-quality-audit-string-68695640 -d '<json body>'
```

Example prompt: Can you audit the quality, normalise the strings, and pivot by date and category for these pipeline duration records — I have run_seconds and processed_rows across dataset_group 'etl-prod' for about 50 rows from last month? Run the analysis on the run_seconds field.

## When to prefer this

Choose this endpoint when you have pipeline duration data (run_seconds, processed_rows) organised by dataset_group and date and need three bundled analytical outputs — quality auditing, string normalisation, and a categorical date pivot — in a single cheap call. It is ideal for data engineering workflows where you want a quick offline audit of up to 200 rows without any external data lookups or predictions.

## Known failure modes

- Input array is empty or exceeds 200 rows — rejected with validation error
- Required fields (row_id, dataset_group, date, run_seconds, processed_rows) missing from row objects
- Date field does not match YYYY-MM-DD pattern — row fails schema validation
- field enum value not one of 'run_seconds' or 'processed_rows' — rejected
- Numeric fields exceed allowed magnitude bounds — row rejected
- Payment of 0.01 USDC not confirmed — request blocked

## How this service works

Pipeline durations — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied pipeline durations records (run_seconds, processed_rows). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports: (1) a record quality audit flagging missing, null, or out-of-range values; (2) a string normalisation report cleaning dataset_group and row_id fields; and (3) a date-by-category pivot summarising the selected numeric field (run_seconds or processed_rows) grouped by date and dataset_group — all 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",
     "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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/pipeline-durations-analytics-record-quality-audit-string-68695640/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)