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Brand Mention Inventories — Adjacent Row Changes + IQR Outlier Flags
x402
Brand Mention Inventories — Adjacent Row Changes + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Analyzes brand mention inventory data by computing adjacent row-to-row changes and flagging IQR-based outliers for mention_count or source_count fields across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied brand mention inventories records (mention_count, source_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
Two independent analytical reports per call: (1) adjacent row-to-row change values for the selected field (mention_count or source_count), and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range — both computed locally on the supplied rows with no external lookups.
Example use cases
Spike detection in PR campaign monitoring
“I've got daily brand mention records for Q3 across two product lines and I want to know which days had mention_count values that are statistical outliers (IQR method) and how much the count shifted from day to day — can you run that analysis on mention_count?”
Source coverage anomaly audit
“Here are 150 rows of our brand mention inventory with date, company_group, mention_count, and source_count — please flag any IQR outliers and compute adjacent row changes for source_count so I can spot unusual coverage gaps.”
Competitor mention trend check
“I have a dataset of daily mention counts for three competitor groups over the past two months. Can you analyze source_count for adjacent-row deltas and IQR outlier flags so I can see where their coverage spiked or dropped abnormally?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-157?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a pre-collected dataset of brand mention records (up to 200 rows) and need fast, local statistical analysis — specifically adjacent row deltas and IQR outlier detection — without requiring live data access, predictions, or external enrichment. Ideal for data quality audits, anomaly triage, and trend review on existing monitoring exports.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, company_group) in any row — validation error
- Invalid date format (must match YYYY-MM-DD pattern)
- All values null for selected field — IQR computation not possible
- Fewer than 1 row supplied — minimum item count not met
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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