{"uid":"cap_4gY_rsKQ66RL4AL0KmICR","slug":"data-freshness-analytics-record-quality-audit-string-normalisation-65f296fa","name":"Data Freshness Analytics: Record Quality Audit + String Normalisation + Category Frequencies","description":"Data freshness — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied data freshness records (age_minutes, record_count). 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-06-195?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}}},"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_wmfSsItvAcMdAJBxedivi","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 — record quality audit, string normalisation, and category frequencies — on up to 200 rows of data freshness records containing age_minutes and record_count fields.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and category frequency analysis on my data freshness logs? Here are up to 200 rows with fields like row_id, dataset_group, date, age_minutes, and record_count — I want all three reports in one call.","exampleUseCases":[{"title":"Pipeline freshness quality audit","prompt":"I have a batch of freshness records from my ETL pipeline — each row has a row_id, dataset_group, date, age_minutes, and record_count. Can you audit them for record quality issues, normalize the string fields, and give me a category frequency breakdown across all dataset groups?"},{"title":"Data observability dashboard enrichment","prompt":"Before I publish my data freshness dashboard, can you run a quality audit, string normalisation, and category frequency report on these 150 freshness rows so I can catch any nulls, casing inconsistencies, or skewed group distributions?"},{"title":"Monitoring stale dataset groups","prompt":"I'm tracking data freshness across multiple dataset groups and want to know which categories appear most often, whether any age_minutes or record_count values look malformed, and if the group name strings need normalising — can you give me all three reports for these records?"}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit flagging missing, null, or out-of-range values in age_minutes and record_count; (2) a string normalisation report standardizing dataset_group and other string fields; and (3) a category frequency table showing how often each dataset_group value appears across the submitted rows. All computed locally with no external lookups.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","age_minutes or record_count outside allowed numeric range — error","Empty rows array — minimum 1 item required"],"whenToPreferThis":"Choose this endpoint when you need all three of record quality auditing, string normalisation, and category frequency analysis on data freshness records in a single low-cost call ($0.01 USDC). It is ideal for ETL monitoring, data observability pipelines, or pre-publish data checks where you have up to 200 rows with age_minutes and record_count fields and want bundled local analytics without any external API calls or live lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:45.786Z","isFirstParty":false,"canonicalSlug":"data-freshness-analytics-record-quality-audit-string-normalisation-65f296fa"}