{"uid":"cap_kgq_lKq3Kq-dBnYN7CWHe","slug":"transformation-throughput-record-quality-audit-string-normalisation-72abf2da","name":"Transformation Throughput — Record Quality Audit + String Normalisation","description":"Transformation throughput — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied transformation throughput records (processed_rows, duration_seconds). 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-13-001?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","processed_rows","duration_seconds"],"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},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"duration_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","processed_rows":10,"duration_seconds":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","processed_rows":20,"duration_seconds":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","processed_rows":15,"duration_seconds":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","processed_rows":30,"duration_seconds":4}],"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_L3S4WkbhIoBmI_4-Zfswi","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":"Audits record quality and normalises strings in transformation throughput data (processed_rows, duration_seconds) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"Can you run a record quality audit and string normalisation on my transformation throughput data? I have rows with fields like row_id, dataset_group, date, processed_rows, and duration_seconds — here are up to 200 records I'd like checked for quality issues and normalised strings.","exampleUseCases":[{"title":"ETL pipeline data quality check","prompt":"I have a batch of transformation throughput records from last week's ETL runs — each row has a row_id, dataset_group, date, processed_rows count, and duration_seconds. Can you audit them for quality issues and normalise the string fields so I know what's clean before I load them into the warehouse?"},{"title":"Dataset group label cleanup","prompt":"My transformation throughput logs have inconsistent dataset_group labels across about 150 rows — things like 'Group-A', 'group_a', and 'GROUP A' mixed together. Can you run string normalisation and a quality audit on these records so I can see what needs fixing?"},{"title":"Pre-reporting data validation","prompt":"Before I build my monthly pipeline performance report, I need to validate these 80 transformation throughput records — checking that processed_rows and duration_seconds values are sensible and that string fields are normalised. Can you give me two reports covering quality and normalisation?"}],"resultDescription":"Two analytical reports generated from local engines: (1) a record quality audit flagging completeness, validity, and consistency issues across the supplied rows; (2) a string normalisation report detailing standardised forms of string fields such as dataset_group. No external lookups or predictions are included.","failureModes":["Empty rows array or missing required fields (row_id, date, processed_rows, duration_seconds) returns a validation error","More than 200 rows submitted exceeds the maxItems limit and is rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Numeric fields outside the allowed range (-1e12 to 1e12) are rejected","Additional properties on row objects are rejected due to additionalProperties: false"],"whenToPreferThis":"Choose this endpoint when you need both a record quality audit and string normalisation in a single call for transformation throughput data specifically (processed_rows + duration_seconds schema). It bundles two reports for $0.01 USDC with no external dependencies, making it ideal for offline or pre-load validation of up to 200 ETL pipeline records. Prefer it over general-purpose validators when your data matches the transformation throughput schema and you want string normalisation alongside quality flagging in one round-trip.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:28.769Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-record-quality-audit-string-normalisation-72abf2da"}