{"uid":"cap_RRVOZ4-GaUa5wziGPdB0i","slug":"transformation-throughput-string-normalisation-iqr-outlier-flags-fe22af7d","name":"Transformation Throughput — String Normalisation + IQR Outlier Flags","description":"Transformation throughput — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags 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-13-032?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_ZF-w-wW5JueIzhePDsG8p","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 for transformation throughput records (processed_rows, duration_seconds) across up to 200 rows, returning 2 independent analytical reports.","exampleAgentPrompt":"Can you run string normalization and IQR outlier detection on my transformation throughput data — here are 5 pipeline runs with row_id, dataset_group, date, processed_rows, and duration_seconds — flag which records are outliers using processed_rows as the target field?","exampleUseCases":[{"title":"ETL pipeline anomaly detection","prompt":"I have daily ETL job logs for two dataset groups from last week — 8 rows with row IDs, dates, processed_rows, and duration_seconds. Can you normalize the group name strings and flag any outlier jobs by processed_rows using IQR analysis?"},{"title":"Data warehouse load monitoring","prompt":"Here are 20 data warehouse load records with row_id, dataset_group, date, processed_rows, and duration_seconds. Run IQR outlier flagging on duration_seconds and normalize the dataset group labels so I can see which loads took abnormally long."},{"title":"Pipeline throughput QA review","prompt":"I need to QA our transformation throughput logs before the sprint review — I have 50 rows with processed_rows and duration_seconds. Can you normalize the string fields and flag statistical outliers using IQR on processed_rows so I can spot bad runs?"}],"resultDescription":"Two independent analytical reports per call: one covering string normalisation of the dataset_group and related string fields, and one flagging per-row IQR outlier status for the selected numeric field (processed_rows or duration_seconds), including computed IQR thresholds and per-row flags across up to 200 input records.","failureModes":["More than 200 rows submitted — request rejected due to maxItems limit","Missing required fields (row_id, date, processed_rows, duration_seconds) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","All values for selected field are null — IQR computation cannot proceed","Numeric values outside ±1e12 range rejected by schema bounds","Malformed JSON payload returns 400-level error"],"whenToPreferThis":"Choose this endpoint when you need both string normalization and IQR-based statistical outlier detection on ETL or data pipeline throughput records in a single call, without requiring external data lookups, ML inference, or live data connections. Ideal for data quality pipelines, pipeline monitoring dashboards, or pre-report data cleaning workflows where processed_rows or duration_seconds anomaly detection is needed on batches up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:02.006Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-string-normalisation-iqr-outlier-flags-fe22af7d"}