{"uid":"cap_Z8KNfggMVMs7n8DkQSZb2","slug":"pipeline-durations-string-normalisation-iqr-outlier-flags-2aea701e","name":"Pipeline Durations — String Normalisation + IQR Outlier Flags","description":"Pipeline durations — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags 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-05-032?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_DKz0rHZhxJ5vAV4IJphfN","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 string fields and flags IQR-based outliers in pipeline duration records (run_seconds, processed_rows), returning two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run string normalisation and IQR outlier detection on these pipeline duration records — I've got 12 rows with row_id, dataset_group, date, run_seconds, and processed_rows — and flag any outliers in the run_seconds field?","exampleUseCases":[{"title":"ETL job anomaly detection","prompt":"I have a batch of 50 pipeline run logs with run_seconds and processed_rows — can you normalize the dataset_group strings and flag any run_seconds values that are IQR outliers so I know which jobs ran unusually long?"},{"title":"Data quality audit before dashboard load","prompt":"Before I load these 120 pipeline duration records into my dashboard, can you run string normalisation on them and use IQR outlier flagging on processed_rows to surface any rows I should investigate?"},{"title":"Weekly pipeline performance review","prompt":"Here are this week's 80 pipeline execution records — run the IQR outlier analysis on run_seconds and normalize the string fields so I can include the flagged anomalies in my weekly performance report."}],"resultDescription":"Two independent analytical reports: one covering string normalisation results for the dataset_group and date fields, and one identifying IQR-based outlier flags for the selected numeric field (run_seconds or processed_rows), covering up to 200 input rows.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, date, run_seconds, processed_rows) cause validation errors","Null-only numeric columns may produce empty or degenerate IQR results","Malformed date strings not matching YYYY-MM-DD pattern fail schema validation","All-identical numeric values produce zero IQR, flagging no outliers"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and IQR-based outlier detection on pipeline timing metrics (run_seconds, processed_rows) in a single call, without requiring external lookups or live data. Ideal for data engineers auditing ETL logs, validating pipeline batch output quality, or preparing cleaned records for downstream reporting — especially when cost efficiency matters at $0.01 for two reports covering up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:14.566Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-string-normalisation-iqr-outlier-flags-2aea701e"}