{"uid":"cap_7PSX7Lh6XOsG9bwhUHJRR","slug":"transformation-throughput-analytics-string-normalisation-adjacent-row-e2a3bca1","name":"Transformation Throughput Analytics — String Normalisation & Adjacent Row Changes","description":"Transformation throughput — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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-13-029?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},"field":{"enum":["processed_rows","duration_seconds"],"type":"string","default":"processed_rows"}}},"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_kIfvPpKYWJtbOGnATs006","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":"Analyzes transformation throughput records to produce string normalisation and adjacent row change reports for up to 200 rows of processed_rows and duration_seconds data.","exampleAgentPrompt":"Can you run a string normalisation and adjacent row changes analysis on my transformation throughput data? I have 4 rows for group-a and group-b from October 2026, with processed_rows values of 10, 20, 15, 30 and duration_seconds of 1, 2, 3, 4 — use processed_rows as the field.","exampleUseCases":[{"title":"ETL pipeline throughput audit","prompt":"I have a week's worth of ETL run logs across two dataset groups — can you normalize the string labels and flag where processed_rows changed significantly between consecutive rows? Here are my 10 records with dates, row IDs, group names, processed_rows, and duration_seconds."},{"title":"Duration anomaly detection across groups","prompt":"I want to spot sudden jumps in duration_seconds between adjacent rows in my transformation logs. I have 50 records across dataset groups alpha and beta from November 2025 — can you run the adjacent row changes report using duration_seconds as the field?"},{"title":"Data quality check before pipeline handoff","prompt":"Before handing off this batch to downstream processing, can you run string normalisation and adjacent row change checks on my 80-row transformation throughput dataset grouped by region and date? I need both reports in one call."}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report that standardizes and cleans string fields across the supplied rows, and (2) an adjacent row changes report that identifies value differences between consecutive rows for the selected numeric field (processed_rows or duration_seconds), all computed locally without external lookups.","failureModes":["Missing required fields (row_id, date, dataset_group, processed_rows, duration_seconds) causes validation error","More than 200 rows submitted exceeds the per-call limit and is rejected","Invalid date format (not YYYY-MM-DD) fails pattern validation","Null or out-of-range numeric values (beyond ±1,000,000,000,000) may cause computation errors","Invalid field enum value (anything other than processed_rows or duration_seconds) returns an error","Payment of 0.01 USDC not included causes x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and adjacent row change detection on transformation throughput records in a single call for up to 200 rows. It is ideal for ETL pipeline auditing, data quality checks before handoffs, and identifying consecutive row anomalies in processed_rows or duration_seconds without requiring external lookups or live data connections.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:56.404Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-analytics-string-normalisation-adjacent-row-e2a3bca1"}