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Account Research Coverage — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Account Research Coverage — Record Quality Audit + String Normalisation + Trailing Mean Series 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).
Runs three local analytical reports (record quality audit, string normalisation, and trailing mean series) on account research coverage data (researched_accounts, target_accounts) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Account research coverage — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied account research coverage records (researched_accounts, target_accounts). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports: (1) a record quality audit flagging completeness, validity, and formatting issues per row; (2) a string normalisation report standardising company_group and other text fields; and (3) a trailing mean series for the specified numeric field (researched_accounts or target_accounts) computed over the given window size — all derived locally from the supplied data, with no external lookups.
Example use cases
Sales coverage data quality check
“I have a list of 80 daily account research coverage rows across several company groups — can you run a record quality audit on them and flag any rows with missing or malformed values? Here's the dataset with row_id, company_group, date, researched_accounts, and target_accounts.”
Normalising inconsistent company group names
“My account research export has messy company_group labels like 'Group-A', 'group a', and 'GROUP A' — can you normalise those strings so they're consistent across all 120 rows?”
Rolling average of researched accounts
“For my 60 rows of daily account research coverage data, can you compute a trailing mean series for researched_accounts using a window of 5, grouped by date?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-08-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical operations — quality audit, string normalisation, and trailing mean series — on account research coverage data in a single low-cost call ($0.01 USDC). Ideal for sales ops teams, revenue analysts, or data pipelines that need to clean and summarise researched_accounts or target_accounts datasets of up to 200 rows without any external data enrichment or live lookups. Prefer this over separate ETL tools when you want a fast, self-contained, offline analysis bundle.
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- date field not matching YYYY-MM-DD pattern — validation error
- numeric values for researched_accounts or target_accounts outside allowed range — schema error
- window value less than 2 or greater than 200 — parameter error
- Missing required fields (row_id, company_group, date) — schema validation failure
- Payment not completed or insufficient USDC — 402 payment required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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