{"uid":"cap__QKcm1s7sfDrc464hfF90","slug":"data-freshness-string-normalisation-exact-row-deduplication-d7a0e03d","name":"Data Freshness — String Normalisation + Exact Row Deduplication","description":"Data freshness — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication 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-06-020?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_7OD_lIBLH7bC_jSRr69Xe","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":"Normalises strings and deduplicates rows in supplied data freshness records (age_minutes, record_count) using local analytical engines, producing 2 independent reports for up to 200 rows.","exampleAgentPrompt":"Can you clean up my data freshness records by normalising string fields and removing exact duplicate rows? I have up to 50 rows with fields like row_id, dataset_group, date, age_minutes, and record_count — I need both the string normalisation and deduplication reports.","exampleUseCases":[{"title":"Pipeline freshness audit deduplication","prompt":"I have a list of data freshness entries from our ETL pipeline — about 80 rows with dataset_group, date, age_minutes, and record_count fields — can you normalise the strings and strip out any exact duplicate rows, then give me both reports?"},{"title":"Dataset group record cleanup","prompt":"We've collected freshness metrics across several dataset groups and I suspect there are duplicate rows and inconsistent string casing. Can you run string normalisation and exact row deduplication on these 120 records and return the two analytical reports?"},{"title":"Data quality check before ingestion","prompt":"Before I ingest these 40 freshness monitoring rows into our warehouse, can you normalise the string columns and deduplicate any identical rows? I need both the normalisation and deduplication reports so I know what was cleaned."}],"resultDescription":"Two independent analytical reports: one covering string normalisation results (standardised string fields across the input rows) and one covering exact row deduplication (identifying and removing duplicate rows), applied to up to 200 supplied data freshness records containing age_minutes and record_count fields.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Payment not included or insufficient (requires 0.01 USDC via x402) — payment error","Null values for age_minutes or record_count may affect deduplication logic"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local string normalisation and exact row deduplication on data freshness records (age_minutes, record_count) without any external lookups, predictions, or live data access. It is ideal for pre-ingestion data cleaning, auditing ETL pipeline freshness logs, or standardising dataset group labels at low cost ($0.01 USDC for 2 reports, up to 200 rows). Not suitable for fuzzy deduplication, ML-based anomaly detection, or live data freshness monitoring.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:11.676Z","isFirstParty":false,"canonicalSlug":"data-freshness-string-normalisation-exact-row-deduplication-d7a0e03d"}