{"uid":"cap_sSUH87YyKxif0WcafmGSn","slug":"pipeline-durations-string-normalisation-adjacent-row-changes-9e2aa50a","name":"Pipeline Durations — String Normalisation + Adjacent Row Changes","description":"Pipeline durations — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied pipeline durations records (run_seconds, processed_rows). 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-05-029?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","run_seconds","processed_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"run_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","run_seconds":10,"dataset_group":"group-a","processed_rows":1},{"date":"2026-10-02","row_id":"row-2","run_seconds":20,"dataset_group":"group-b","processed_rows":2},{"date":"2026-10-03","row_id":"row-3","run_seconds":15,"dataset_group":"group-a","processed_rows":3},{"date":"2026-10-04","row_id":"row-4","run_seconds":30,"dataset_group":"group-b","processed_rows":4}],"maxItems":200,"minItems":1},"field":{"enum":["run_seconds","processed_rows"],"type":"string","default":"run_seconds"}}},"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_3EGkyo9kmGRe_oVB6Y-It","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 two local analytical reports on pipeline duration records: string normalisation and adjacent row change detection, for up to 200 rows of run_seconds or processed_rows data.","exampleAgentPrompt":"Can you run string normalisation and adjacent row change detection on these pipeline duration records? I have 4 rows with run_seconds values — row-1 on 2026-10-01 (10s, group-a), row-2 on 2026-10-02 (20s, group-b), row-3 on 2026-10-03 (15s, group-a), row-4 on 2026-10-04 (30s, group-b) — and I want to analyse the run_seconds field.","exampleUseCases":[{"title":"Detect pipeline slowdowns between runs","prompt":"I have a week of daily pipeline run records with run_seconds logged per dataset group — can you check for adjacent row changes so I can spot where durations jumped between consecutive days? Here are my 10 rows with row IDs, dates, dataset groups, and run_seconds values."},{"title":"Normalise processed row counts across groups","prompt":"Can you run string normalisation on my processed_rows data across these 50 pipeline records? I want to standardise the values so I can compare group-a and group-b output volumes meaningfully — I'll send you the full rows with row_id, date, dataset_group, and processed_rows."},{"title":"Audit ETL batch duration consistency","prompt":"I want two reports on my ETL pipeline logs: string normalisation and adjacent row changes for the run_seconds field. I have about 150 rows covering last quarter, each with a date, row_id, dataset_group, and run_seconds — can you run both analyses at once?"}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report standardising the selected numeric field (run_seconds or processed_rows) across supplied rows, and (2) an adjacent row changes report identifying value differences between consecutive rows, all computed locally without external lookups.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, date, run_seconds, processed_rows) on any row — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Field selector not one of 'run_seconds' or 'processed_rows' — enum validation error","Numeric values outside allowed range (-1e12 to 1e12) — schema validation error","Empty rows array — minimum items constraint violated"],"whenToPreferThis":"Choose this endpoint when you need two complementary analytical passes — string normalisation and sequential row change detection — on pipeline duration data (run_seconds or processed_rows) in a single call. It is ideal for ETL monitoring, data quality audits, or pipeline performance reviews where you want both a normalised view and a row-to-row delta report without any external API calls or live predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:03.848Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-string-normalisation-adjacent-row-changes-9e2aa50a"}