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Loot Observations — Numeric Row Ordering + Secondary-Measure Weighted Mean
x402
Loot Observations — Numeric Row Ordering + Secondary-Measure Weighted Mean 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).
Computes numeric row ordering and secondary-measure weighted mean analytics on up to 200 loot observation records (drop_count, attempt_count) using local analytical engines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Loot observations — Numeric row ordering + Secondary-measure weighted mean: Numeric row ordering + Secondary-measure weighted mean 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 two independent analytical reports: (1) numeric row ordering of the supplied loot observation rows ranked by the chosen field, and (2) a secondary-measure weighted mean computed across the dataset. Both reports are derived locally from the supplied data with no external lookups.
Example use cases
Rank loot drop rates by zone
“Here are 8 loot observation records from different dungeon zones over the past week with drop_count and attempt_count — rank them numerically by drop_count and give me the attempt_count weighted mean so I know which zones are most efficient.”
Weighted mean for attempt analysis
“I've collected 50 rows of loot farming data with row IDs, game areas, dates, drop counts, and attempt counts — compute the numeric row ordering and secondary-measure weighted mean using attempt_count as the field.”
Compare drop efficiency across areas
“I have loot observations from group-a and group-b over the last month, up to 150 rows, with drop_count and attempt_count filled in — run the numeric row ordering and weighted mean analysis on drop_count so I can compare farming efficiency across those two areas.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-05-121?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular loot observation data (drop_count, attempt_count) and need both row-level numeric ranking and a weighted mean statistic in a single call for up to 200 rows. Prefer it over general-purpose analytics when data is already structured in the required schema and you want a low-cost ($0.01 USDC) dual-report result without external data lookups or predictions.
Side effects
- Exceeding 200 rows returns an error — trim input to at most 200 records
- Missing required fields (row_id, game_area, date, drop_count, attempt_count) cause validation failure
- Invalid date format (not YYYY-MM-DD) triggers a pattern mismatch error
- Null values in drop_count or attempt_count may affect weighted mean accuracy
- Invalid field enum value (must be drop_count or attempt_count) causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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