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Loot Observations — String Normalisation + Adjacent Row Changes
x402
Loot Observations — String Normalisation + 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 2026-10-30).
Normalises game_area strings and computes adjacent row changes for loot observation records (drop_count or attempt_count) across up to 200 rows, returning 2 analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Loot observations — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied loot observations records (drop_count, attempt_count). 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 2 independent analytical reports: one covering string normalisation of the game_area field across all submitted rows, and one showing adjacent (row-to-row) changes in the selected numeric field (drop_count or attempt_count). No external lookups or predictions are included.
Example use cases
Daily loot drop delta tracking
“I have daily loot data for two game areas over the past week — can you normalise the area names and show me how drop_count changed from one day to the next? Here are 7 rows with row IDs row-1 through row-7, dates 2026-10-01 to 2026-10-07, alternating game areas 'Zone A' and 'zone-a', and drop counts 5, 12, 9, 20, 18, 25, 30, all with attempt_count 1.”
Attempt count trend analysis for game sessions
“Can you run string normalisation and adjacent row changes on my attempt_count field for these loot observation records? I've got 6 rows: row-1 through row-6, dates 2026-11-01 to 2026-11-06, all in game_area 'dungeon-1', attempt_counts 3, 7, 5, 12, 10, 15, and drop_counts all set to 0.”
Multi-area loot consistency audit
“I need to clean up inconsistent game area labels and find row-to-row swings in drop_count across these 10 loot observation rows spanning two areas called 'Boss Arena' and 'boss_arena' — dates run from 2026-09-01 to 2026-09-10, row IDs row-1 to row-10, drop_counts ranging 50 to 200, and attempt_counts 5 to 20.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-05-029?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation of categorical game area labels and adjacent row change computation on loot metrics in a single low-cost call. It is ideal for game analytics pipelines processing sequential loot observation data without needing live lookups, external enrichment, or predictions. At $0.01 USDC for 2 reports on up to 200 rows, it is cost-effective for batch offline analysis of game session logs.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, game_area, date, drop_count, attempt_count) cause rejection
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation error
- Numeric values outside ±1,000,000,000,000 are rejected
- Invalid field enum value (anything other than drop_count or attempt_count) causes an error
- Payment failure or insufficient USDC balance prevents processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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