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Cache Observations – Adjacent Row Changes
x402
Cache Observations – 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 2026-10-15).
Computes adjacent (period-over-period) row changes for cache hit/miss observation data, returning a bounded descriptive analytics report for up to 200 records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Cache observations - Adjacent row changes: Adjacent row changes for Cache observations. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded adjacent row changes report.
Output
A bounded descriptive analytics report showing the computed adjacent (row-to-row) change values for the selected metric field (hits or misses) across the supplied cache observation records, with no forecasting or external enrichment applied.
Example use cases
Daily cache hit trend detection
“I have a week of daily cache observation records for two cache groups with hit and miss counts — can you compute the adjacent row changes on the hits field so I can see how much the hit count shifted each day?”
Cache miss spike investigation
“My cache monitoring logs show misses going up and down — can you run an adjacent row changes report on the misses field across these 10 dated records so I can spot which consecutive days had the biggest spikes?”
Period-over-period cache performance summary
“I'm preparing a performance review and need a bounded descriptive report showing how cache hits changed between each consecutive observation in my dataset of about 50 rows — can you calculate the adjacent row changes on the hits metric?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-cache-observations-period-change?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, local, period-over-period delta report on cache hit or miss timeseries data without any enrichment, forecasting, or external data dependencies. It is ideal for lightweight analytical pipelines where you supply up to 200 typed cache observation records and only need adjacent row change values at minimal cost ($0.01 USDC).
Side effects
- Fewer than 1 row supplied — minItems validation failure
- More than 200 rows supplied — maxItems exceeded
- Missing required fields (row_id, cache, date, hits, misses) on any record
- Invalid date format (must match YYYY-MM-DD pattern)
- Numeric values outside allowed range (±1,000,000,000,000)
- Invalid field enum value (must be 'hits' or 'misses')
- Payment not fulfilled — x402 payment required before response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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