# Simulation Caches — Numeric Interval Filter + Adjacent Row Changes

> Simulation Caches — Numeric Interval Filter + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-21).

Filters simulation cache rows by a numeric interval on cache_gb or simulation_seconds, and computes adjacent row changes, returning two analytical reports for up to 200 supplied rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-08-127?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-21
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/simulation-caches-numeric-interval-filter-adjacent-row-changes-eb7a1ca9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_D4BcyqSaRqqvOUJRQdhNj

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 simulation-caches-numeric-interval-filter-adjacent-row-changes-eb7a1ca9 -d '<json body>'
```

Example prompt: I have a list of simulation cache records with cache_gb and simulation_seconds values — can you filter for rows where cache_gb is between 10 and 50, and also show me the adjacent row changes for that field? Here are up to 200 rows with row_id, asset_group, and date included.

## When to prefer this

Choose this endpoint when you have up to 200 simulation cache records with cache_gb and simulation_seconds columns and need both numeric interval filtering and adjacent row delta analysis in a single call. It is ideal when you want to identify which simulation entries fall within a specific size or duration range while simultaneously tracking row-over-row changes, without any live data lookup or external enrichment. Prefer this over general-purpose analytics tools when your data fits the simulation caches schema and you want a low-cost ($0.01 USDC), fast, self-contained dual-report output.

## Known failure modes

- Input array is empty or exceeds 200 rows — validation error
- Minimum value is greater than maximum value — invalid interval error
- Required row fields (row_id, date, cache_gb, simulation_seconds, asset_group) missing — schema validation error
- Field enum value not recognized (must be cache_gb or simulation_seconds) — invalid field error
- Numeric values outside allowed range (-1e12 to 1e12) — validation error
- Date field does not match YYYY-MM-DD pattern — format error

## How this service works

Simulation caches — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes for supplied simulation caches records (cache_gb, simulation_seconds). 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 analytical reports: (1) rows from the input dataset whose specified numeric field (cache_gb or simulation_seconds) falls within the provided minimum–maximum interval, and (2) adjacent row changes (deltas) for that same numeric field across all supplied rows. 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",
     "asset_group",
     "date",
     "cache_gb",
     "simulation_seconds"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "cache_gb": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "asset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "simulation_seconds": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "cache_gb": 10,
     "asset_group": "group-a",
     "simulation_seconds": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "cache_gb": 20,
     "asset_group": "group-b",
     "simulation_seconds": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "cache_gb": 15,
     "asset_group": "group-a",
     "simulation_seconds": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "cache_gb": 30,
     "asset_group": "group-b",
     "simulation_seconds": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "cache_gb",
    "simulation_seconds"
   ],
   "type": "string",
   "default": "cache_gb"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/simulation-caches-numeric-interval-filter-adjacent-row-changes-eb7a1ca9/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)