# Data Reconciliation — Daily Sum Series + Base-100 Series

> Data Reconciliation — Daily Sum Series + Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Generates two analytical reports (daily sum time series and base-100 indexed series) from supplied source/target reconciliation data, up to 200 rows, for $0.01 USDC.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-138?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-reconciliation-daily-sum-series-base-100-series-4c8e1ec1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_iIcCSImOUbGOQ5CLY7fFW

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 data-reconciliation-daily-sum-series-base-100-series-4c8e1ec1 -d '<json body>'
```

Example prompt: I have 4 reconciliation rows across two dataset groups (group-a and group-b) from 2026-10-01 to 2026-10-04 with source_total values 10, 20, 15, 30 and target_total values 1, 2, 3, 4 — can you run both the daily sum series and the base-100 series reports on the source_total field, rebasing from row index 0?

## When to prefer this

Choose this endpoint when you need two complementary time-series views — a daily aggregate sum and a normalized base-100 index — from reconciliation data you already have locally (source_total and target_total), without needing external lookups or predictions. It's ideal for batch reconciliation analysis at very low cost ($0.01 for both reports). Prefer it over single-report endpoints when you need both raw daily sums and relative indexed trends in one call.

## Known failure modes

- More than 200 rows submitted — request rejected, reduce to 200 or fewer rows
- Missing required fields (row_id, dataset_group, date, source_total, target_total) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric values exceed ±1,000,000,000,000 — out-of-range error
- base_index references a row index that doesn't exist — index out of bounds error
- Payment of 0.01 USDC not completed via x402 — payment required error

## How this service works

Data reconciliation — Daily sum series + Base-100 series: Daily sum series + Base-100 series for supplied data reconciliation records (source_total, target_total). 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 independent analytical reports computed locally from your supplied data: (1) a daily sum time series aggregating source_total or target_total by date, and (2) a base-100 indexed series normalizing values relative to a chosen base row, 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",
     "source_total",
     "target_total"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_total": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "target_total": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_total": 10,
     "target_total": 1,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_total": 20,
     "target_total": 2,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_total": 15,
     "target_total": 3,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_total": 30,
     "target_total": 4,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "source_total",
    "target_total"
   ],
   "type": "string",
   "default": "source_total"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-daily-sum-series-base-100-series-4c8e1ec1/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)