{"uid":"cap_EJ_bFlWzFGxjBV6doMvnK","slug":"data-freshness-record-quality-audit-numeric-distribution-62edcec7","name":"Data Freshness Record Quality Audit + Numeric Distribution","description":"Data freshness — Record quality audit + Numeric distribution: Record quality audit + Numeric distribution for supplied data freshness records (age_minutes, record_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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-004?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","age_minutes","record_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"age_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"record_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","age_minutes":10,"record_count":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","age_minutes":20,"record_count":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","age_minutes":15,"record_count":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","age_minutes":30,"record_count":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["age_minutes","record_count"],"type":"string","default":"age_minutes"}}},"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_jyLWwbdLoKumCmoBb0BHD","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 two local analytical reports — a record quality audit and a numeric distribution analysis — on up to 200 supplied data freshness rows containing age_minutes and record_count fields.","exampleAgentPrompt":"Can you run a record quality audit and numeric distribution report on these data freshness rows? I have up to 50 rows with date, row_id, dataset_group, age_minutes, and record_count — analyze the age_minutes field and give me both reports.","exampleUseCases":[{"title":"Pipeline freshness quality audit","prompt":"I have a batch of 80 data freshness records from my ETL pipeline — each row has a date, dataset group, age_minutes, and record_count. Can you run a record quality audit and show me a numeric distribution of the age_minutes values so I can spot any stale or bad records?"},{"title":"Record count distribution profiling","prompt":"Can you analyze these 120 data freshness rows and give me a quality audit plus a numeric distribution of the record_count field? I want to understand the spread and flag any problematic rows across my dataset groups."},{"title":"Data freshness monitoring check","prompt":"Here are 40 freshness tracking rows from our monitoring system, each with a row_id, date, dataset_group, age_minutes, and record_count. Please run both a record quality audit and a numeric distribution on age_minutes so I can see where data freshness is degrading."}],"resultDescription":"Returns two independent analytical reports: (1) a record quality audit highlighting issues such as nulls, out-of-range values, and anomalies in the supplied rows, and (2) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram bins) for the selected numeric field (age_minutes or record_count). Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds maximum row limit","Missing required fields (row_id, dataset_group, date, age_minutes, record_count) in any row — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","field parameter not one of 'age_minutes' or 'record_count' — enum validation error","Numeric values outside allowed range (-1000000000000 to 1000000000000) — rejected"],"whenToPreferThis":"Choose this endpoint when you have a batch of up to 200 data freshness records and need two quick analytical outputs — a record quality audit and a numeric distribution — without requiring any external data lookup, live API, or prediction. It is best suited for offline profiling of freshness monitoring data (age_minutes, record_count) grouped by dataset and date. Prefer it over general-purpose analytics when you specifically want these two combined report types at low cost ($0.01 USDC for both).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:38.150Z","isFirstParty":false,"canonicalSlug":"data-freshness-record-quality-audit-numeric-distribution-62edcec7"}