{"uid":"cap_kK-RG6nkwOQebMfcbnucf","slug":"transformation-throughput-string-normalisation-duplicate-key-report-5ccddb4a","name":"Transformation Throughput — String Normalisation + Duplicate Key Report","description":"Transformation throughput — String normalisation + Duplicate key report: String normalisation + Duplicate key report 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-13-021?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_rGFvv_I3VdQfRb5kkKu0R","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 generates a duplicate key report for transformation throughput records (processed_rows, duration_seconds) across up to 200 rows.","exampleAgentPrompt":"Can you normalise the string fields and generate a duplicate key report for my transformation throughput data? I have 4 rows: row-1 for group-a on 2026-10-01 with 10 processed rows in 1 second, row-2 for group-b on 2026-10-02 with 20 processed rows in 2 seconds, row-3 for group-a on 2026-10-03 with 15 processed rows in 3 seconds, and row-4 for group-b on 2026-10-04 with 30 processed rows in 4 seconds.","exampleUseCases":[{"title":"ETL pipeline duplicate key audit","prompt":"I have 50 rows of transformation throughput logs with row IDs, dataset groups, dates, processed_rows counts and duration_seconds — can you normalise the string fields and tell me which row keys appear more than once?"},{"title":"Data quality check before warehouse load","prompt":"Before I load my pipeline metrics into the warehouse, can you clean up the string fields and produce a duplicate key report for these 120 transformation throughput records? Each row has a row_id, dataset_group, date, processed_rows and duration_seconds."},{"title":"Multi-group throughput string normalisation","prompt":"My team has been entering dataset_group names inconsistently across 80 rows of transformation throughput data. Can you normalise those strings and also flag any duplicate row IDs so I know which ones to fix?"}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report showing cleaned/standardised string field values across the supplied rows, and (2) a duplicate key report identifying any row_id or key fields that appear more than once — all computed locally from the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, processed_rows, duration_seconds) causes a schema rejection","Invalid date format (not YYYY-MM-DD) triggers a pattern validation error","Submitting zero rows (minItems: 1 violated) returns an error","Extremely large numeric values outside ±1,000,000,000,000 are rejected"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and duplicate key detection on transformation throughput records (processed_rows, duration_seconds) in a single low-cost call. It is ideal for data engineering pipelines, ETL audit workflows, or pre-load data quality checks where you want two reports for the price of one call on up to 200 rows, without needing live lookups or external enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:00.993Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-string-normalisation-duplicate-key-report-5ccddb4a"}