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

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

Runs three independent analytical reports (record quality audit, exact row deduplication, and numeric row ordering) on time series completeness data containing observed and expected point counts, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-213?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-8e587a9e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_SlUWfPkZaSaUL0SQrTn7d

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-8e587a9e -d '<json body>'
```

Example prompt: Can you run a record quality audit, exact deduplication check, and numeric row ordering on my time series completeness data? I have rows with row_id, dataset_group, date, observed_points, and expected_points — up to 50 rows — and I want the analysis run on the observed_points field.

## When to prefer this

Choose this endpoint when you need three bundled analytical operations — quality audit, deduplication, and numeric ordering — applied to time series completeness records in a single $0.01 call, without requiring any external data lookup or live prediction. It is best suited for offline/batch validation of datasets up to 200 rows where observed_points and expected_points are the key numeric dimensions.

## Known failure modes

- Exceeding 200 rows returns an error — batch must be split
- Missing required fields (row_id, date, observed_points, expected_points) cause validation failure
- Date strings not matching YYYY-MM-DD pattern are rejected
- Numeric values outside the allowed range (-1e12 to 1e12) cause schema rejection
- Invalid field enum value (anything other than observed_points or expected_points) returns an error

## How this service works

Time series completeness — Record quality audit + Exact row deduplication + Numeric row ordering: Record quality audit + Exact row deduplication + Numeric row ordering for supplied time series completeness records (observed_points, expected_points). 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 for the submitted rows: (1) a record quality audit flagging malformed or anomalous entries, (2) an exact row deduplication report identifying and flagging duplicate rows, and (3) a numeric row ordering report ranking rows by the selected numeric field (observed_points or expected_points). All computed locally without 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
  },
  "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-exact-row-deduplication-8e587a9e/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)