{"uid":"cap_Y5bQ6kzZ35j6dkJXHqDIq","slug":"duplicate-observations-string-normalisation-iqr-outlier-flags-b7019113","name":"Duplicate Observations — String Normalisation + IQR Outlier Flags","description":"Duplicate observations — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied duplicate observations records (duplicate_rows, total_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-04-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","duplicate_rows","total_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"duplicate_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_rows":1,"dataset_group":"group-a","duplicate_rows":10},{"date":"2026-10-02","row_id":"row-2","total_rows":2,"dataset_group":"group-b","duplicate_rows":20},{"date":"2026-10-03","row_id":"row-3","total_rows":3,"dataset_group":"group-a","duplicate_rows":15},{"date":"2026-10-04","row_id":"row-4","total_rows":4,"dataset_group":"group-b","duplicate_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["duplicate_rows","total_rows"],"type":"string","default":"duplicate_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_61H0ANfM6yLberFmBoyuo","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 string normalisation and IQR-based outlier detection on up to 200 duplicate-observation records (duplicate_rows, total_rows), returning two analytical reports per call.","exampleAgentPrompt":"I have a batch of up to 50 duplicate observation records — each with a row_id, dataset_group, date, duplicate_rows, and total_rows — can you run string normalisation and IQR outlier flagging on the duplicate_rows field and give me both analytical reports?","exampleUseCases":[{"title":"Daily data pipeline duplicate audit","prompt":"Here are 120 records from our daily ingestion pipeline, each with a dataset_group, date, duplicate_rows, and total_rows. Can you normalise the string fields and flag any IQR outliers in the duplicate_rows column so I can spot which days had abnormal duplication?"},{"title":"Cross-group dataset quality check","prompt":"I've got 80 observations across several dataset groups with their duplicate_rows and total_rows counts. Please run the IQR outlier flags on total_rows and also normalise the string fields — I want to see which groups are statistical anomalies."},{"title":"Historical duplicate trend anomaly detection","prompt":"We have 6 months of weekly duplicate_rows and total_rows readings per dataset group. Can you apply string normalisation and IQR outlier detection on the duplicate_rows field across all 200 rows and return the two analytical reports?"}],"resultDescription":"Two analytical reports per call: (1) a string normalisation report cleaning and standardising text fields in the supplied records, and (2) an IQR outlier flag report identifying statistically anomalous values in the selected numeric field (duplicate_rows or total_rows) using interquartile range analysis. Results are based entirely on the supplied data with no external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) causes schema rejection","Dates not matching YYYY-MM-DD pattern fail validation","Supplying fewer than 1 row triggers minItems constraint error","Numeric values outside ±1,000,000,000,000 fail schema validation","Invalid field enum value (anything other than duplicate_rows or total_rows) rejected"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and IQR-based outlier detection on duplicate observation records in a single low-cost call. It is ideal for data quality pipelines that track duplicate_rows and total_rows metrics across dataset groups over time, and when you need two independent analytical reports from a single batch of up to 200 rows. Prefer it over general-purpose analytics tools when your input is specifically structured duplicate-observation data with dataset_group, date, and row count fields.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T06:40:14.116Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-string-normalisation-iqr-outlier-flags-b7019113"}