# Time Series Completeness — Record Quality Audit + Exact Row Deduplication

> Time Series Completeness — Record Quality Audit + Exact Row Deduplication 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).

Audits up to 200 time series completeness records (observed_points vs expected_points) for data quality issues and exact duplicate rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-002?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/time-series-completeness-record-quality-audit-exact-row-deduplication-6a88827e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_otouUX2FS6fr4CeCPL23Q

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-exact-row-deduplication-6a88827e -d '<json body>'
```

Example prompt: Can you run a record quality audit and exact row deduplication check on this batch of time series completeness data? I have rows for group-a and group-b from 2026-10-01 through 2026-10-04, each with observed_points and expected_points values — give me both reports.

## When to prefer this

Choose this endpoint when you need both a record quality audit and exact deduplication report for time series completeness data (observed vs expected data points) in a single $0.01 call, without requiring any live lookups or external data. It is ideal for pre-ingestion data validation pipelines, ETL quality gates, or ad-hoc audits of up to 200 rows. Prefer alternatives if you need predictive analytics, outlier detection (IQR-based), or metrics beyond observed/expected point counts.

## Known failure modes

- More than 200 rows submitted — exceeds maximum batch size
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) on any row — validation error
- Date field not matching YYYY-MM-DD pattern — schema rejection
- row_id or dataset_group exceeds 80 character limit — schema rejection
- observed_points or expected_points outside allowed numeric range — schema rejection
- Payment failure or insufficient USDC balance — request rejected

## How this service works

Time series completeness — Record quality audit + Exact row deduplication: Record quality audit + Exact row deduplication 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

Returns two independent reports: (1) a record quality audit identifying issues in the supplied time series completeness records (e.g. nulls, format violations, anomalies), and (2) an exact row deduplication report flagging rows that are identical across all fields. Both reports are computed locally from the supplied data with no external lookups.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-record-quality-audit-exact-row-deduplication-6a88827e/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)