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Feature Availability Analytics: Record Quality Audit + String Normalisation + Exact Row Deduplication
x402
Feature Availability Analytics: Record Quality Audit + String Normalisation + Exact Row Deduplication is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs three local analytical reports on feature availability records — record quality audit, string normalisation, and exact row deduplication — for up to 200 rows of populated/expected feature data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication for supplied feature availability records (populated_features, expected_features). 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
Three independent analytical reports delivered together: (1) a record quality audit flagging missing, null, or out-of-range values in the feature availability rows; (2) a string normalisation report identifying and correcting inconsistencies in text fields like dataset_group; (3) an exact row deduplication report identifying and removing identical rows. All analysis is performed locally with no external lookups or predictions.
Example use cases
Pre-pipeline feature dataset cleanup
“Before I load my feature availability data into the warehouse, can you audit it for quality issues, normalise the dataset_group strings, and remove any exact duplicate rows? I have about 80 rows with dates in 2026, row IDs like 'feat-001', groups like 'vision' and 'nlp', and populated_features and expected_features counts.”
Tracking feature completion gaps across teams
“I want to check data quality across my feature tracking records — can you run all three reports (quality audit, string normalisation, deduplication) on this batch of 120 rows where each row has a date, dataset_group like 'team-alpha', and populated vs expected feature counts?”
Investigating inconsistent feature reporting
“Our feature availability reporting has been messy — string casing is inconsistent and I suspect there are duplicate rows. Can you normalise the strings, deduplicate exact rows, and give me a quality audit across these 60 feature availability records with fields for row_id, dataset_group, date, populated_features, and expected_features?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-191?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three standard data quality operations — quality audit, string normalisation, and exact deduplication — applied together to feature availability records in a single low-cost call. It is ideal for pre-ingestion data cleaning pipelines, routine quality checks on feature tracking datasets, or any scenario where populated vs expected feature counts need validation across up to 200 dated rows. It does not perform ML-based fuzzy deduplication, external enrichment, or predictive analytics.
Side effects
- Exceeding 200 rows will be rejected (maxItems: 200)
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) will cause validation errors
- Date strings not matching YYYY-MM-DD pattern will fail schema validation
- populated_features or expected_features values outside ±1,000,000,000,000 will be rejected
- Empty rows array (minItems: 1) will be rejected
- Payment failure or insufficient USDC balance will prevent execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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