{"uid":"cap_Nki_Gl_TE026FJ7OfnM7l","slug":"feature-availability-analytics-record-quality-audit-string-93f81826","name":"Feature Availability Analytics — Record Quality Audit, String Normalisation & Duplicate Key Report","description":"Feature availability — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-192?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_F1p0kgwxEjvGld3it5v0D","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Runs three independent analytical reports (record quality audit, string normalisation, and duplicate key detection) on supplied feature availability records (populated_features vs expected_features) for up to 200 rows.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and duplicate key check on my feature availability data? I have rows with row_id, dataset_group (like 'group-a' and 'group-b'), dates like 2026-10-01, and populated_features vs expected_features counts — up to 50 rows total.","exampleUseCases":[{"title":"Pre-pipeline data quality gate","prompt":"Before I load this feature availability dataset into our ML pipeline, can you audit it for record quality issues, normalise any inconsistent string values in dataset_group, and flag any duplicate row IDs? I'll pass you the rows with their dates, populated_features, and expected_features counts."},{"title":"Duplicate key investigation in feature store","prompt":"I think my feature store export has some duplicate row keys mixed in. Can you run a duplicate key report on these 80 feature availability records — each has a row_id, dataset_group, date, and populated_features and expected_features numbers?"},{"title":"Monthly feature coverage QA review","prompt":"I need to do a quality review of our October feature availability records — can you check them for data quality issues, string normalisation problems in the group labels, and any duplicate keys? I have about 120 rows across two dataset groups with dates and populated vs expected feature counts."}],"resultDescription":"Three independent analytical reports: (1) a record quality audit highlighting anomalies or missing/invalid values in the supplied rows, (2) a string normalisation report identifying and standardising inconsistent string values (e.g. in dataset_group), and (3) a duplicate key report flagging rows with repeated row_id or key combinations — all computed locally with no external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, date, populated_features, expected_features) cause row-level rejection","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Null values in populated_features or expected_features may affect quality audit completeness","Payment of 0.01 USDC not included or rejected results in HTTP 402"],"whenToPreferThis":"Choose this endpoint when you need all three of record quality auditing, string normalisation, and duplicate key detection bundled together for feature availability data in a single low-cost call. It is ideal for batch data validation of up to 200 rows before ingestion, ETL quality gates, or periodic QA reviews — especially when no live lookup, prediction, or external enrichment is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:05.318Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-record-quality-audit-string-93f81826"}