{"uid":"cap_WPkTms50NqXTlI4mCUBIR","slug":"feature-availability-analytics-quality-audit-deduplication-numeric-14028e8f","name":"Feature Availability Analytics — Quality Audit, Deduplication & Numeric Row Ordering","description":"Feature availability — Record quality audit + Exact row deduplication + Numeric row ordering: Record quality audit + Exact row deduplication + Numeric row ordering 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-10-213?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},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"}}},"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_nLaOWDCP-FQMfuS81hnBm","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 local analytical reports on feature availability records: record quality audit, exact row deduplication, and numeric row ordering — all in a single call for up to 200 rows.","exampleAgentPrompt":"Can you run a quality audit, exact deduplication check, and numeric row ordering on my feature availability records? I have rows with row_id, dataset_group, date, populated_features, and expected_features — up to 50 rows across two groups, and I want to order by populated_features.","exampleUseCases":[{"title":"Feature completeness audit for ML pipeline","prompt":"I have a batch of feature availability records from my ML pipeline — each row has a row_id, dataset_group, date, populated_features count, and expected_features count. Can you run a quality audit and flag any data issues, plus deduplicate them and order them by populated_features?"},{"title":"Deduplication check on dataset group records","prompt":"We've been collecting feature availability logs across dataset groups like 'group-a' and 'group-b' and I suspect there are duplicate rows. Can you deduplicate these records and also give me a quality audit and numeric ordering by expected_features for the full set?"},{"title":"Pre-analysis data cleaning for feature store","prompt":"Before I load these feature availability records into our feature store, I need to clean them up — please run a quality audit, remove exact duplicates, and sort them numerically by populated_features so I can review the output."}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging data issues across the supplied rows, (2) an exact row deduplication report identifying and removing duplicate records, and (3) a numerically ordered listing of rows ranked by the selected numeric field (populated_features or expected_features). All computed locally without external lookups.","failureModes":["Fewer than 1 row provided — minimum 1 row required","More than 200 rows submitted — maxItems limit exceeded","Invalid date format — must match YYYY-MM-DD pattern","row_id missing or empty — required field","populated_features or expected_features outside allowed numeric range","Numeric values provided as strings instead of numbers or null"],"whenToPreferThis":"Use this endpoint when you need a bundled, cost-efficient single-call solution for three common data hygiene tasks — quality auditing, deduplication, and numeric ordering — specifically on feature availability records with populated_features and expected_features fields. Ideal for pre-processing steps before loading data into feature stores or ML pipelines, where paying $0.01 for all three reports at once is more efficient than calling separate endpoints.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:54.261Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-quality-audit-deduplication-numeric-14028e8f"}