# Time Series Completeness — Record Quality Audit + Date by Category Pivot

> Time Series Completeness — Record Quality Audit + 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 2026-10-28).

Runs two local analytical reports on time series completeness data: a record quality audit and a date-by-category pivot, for up to 200 rows of observed vs expected points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-017?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-record-quality-audit-date-by-category-pivot-d0dd2500
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Il8VOHt82A3t1WgwQQ2kk

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 time-series-completeness-record-quality-audit-date-by-category-pivot-d0dd2500 -d '<json body>'
```

Example prompt: Can you run a record quality audit and a date-by-category pivot on my completeness data? I have rows with fields like dataset_group, date, observed_points, and expected_points — about 50 rows covering groups 'sensor-A' and 'sensor-B' across October 2026, and I want to analyze the observed_points field.

## When to prefer this

Choose this endpoint when you need two simultaneous local analytical reports — a record quality audit and a date-by-category pivot — on time series completeness data with observed and expected point counts. It is ideal for batch offline analysis of up to 200 rows without requiring any external data sources, predictions, or live lookups. Prefer it over single-report endpoints when both audit and pivot insights are needed in one call at minimal cost.

## Known failure modes

- Fewer than 1 or more than 200 rows supplied — request rejected
- Missing required fields (row_id, date, dataset_group, observed_points, expected_points) — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- observed_points or expected_points outside allowed numeric range — rejection
- Invalid field enum value (not 'observed_points' or 'expected_points') — error response

## How this service works

Time series completeness — Record quality audit + Date by category pivot: Record quality audit + Date by category pivot for supplied time series completeness records (observed_points, expected_points). 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

Two analytical reports derived entirely from the supplied data: (1) a record quality audit flagging anomalies, nulls, or integrity issues per row, and (2) a date-by-category pivot summarizing observed or expected points across dataset groups and dates. No external lookups or predictions are made.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "observed_points",
     "expected_points"
    ],
    "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
     },
     "expected_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "observed_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_points": 1,
     "observed_points": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_points": 2,
     "observed_points": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_points": 3,
     "observed_points": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_points": 4,
     "observed_points": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "observed_points",
    "expected_points"
   ],
   "type": "string",
   "default": "observed_points"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-record-quality-audit-date-by-category-pivot-d0dd2500/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)