{"uid":"cap_KZGPofkcyoKP1i-DPQI3I","slug":"sampling-observations-string-normalisation-and-iqr-outlier-flags-67ad87f2","name":"Sampling Observations String Normalisation and IQR Outlier Flags","description":"Sampling observations — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied sampling observations records (sample_value, sample_weight). 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-15-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","sample_value","sample_weight"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"sample_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","sample_value":10,"dataset_group":"group-a","sample_weight":1},{"date":"2026-10-02","row_id":"row-2","sample_value":20,"dataset_group":"group-b","sample_weight":2},{"date":"2026-10-03","row_id":"row-3","sample_value":15,"dataset_group":"group-a","sample_weight":3},{"date":"2026-10-04","row_id":"row-4","sample_value":30,"dataset_group":"group-b","sample_weight":4}],"maxItems":200,"minItems":1},"field":{"enum":["sample_value","sample_weight"],"type":"string","default":"sample_value"}}},"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_vfWCGSCP7fTuD_hevocxP","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 across up to 200 sampling observation rows (sample_value, sample_weight), producing two analytical reports per call.","exampleAgentPrompt":"Can you run string normalisation and IQR outlier flagging on my sampling observations? I have rows with row_id, dataset_group, date, sample_value, and sample_weight — up to 50 rows — and I want to check the sample_value field for outliers and get both analytical reports back.","exampleUseCases":[{"title":"Survey data outlier audit","prompt":"I've got 120 rows of survey sample data with sample_value and sample_weight columns. Can you normalize the string fields and flag any IQR outliers in the sample_value field so I can see which records look anomalous?"},{"title":"Weighted measurement quality check","prompt":"Run IQR outlier detection on my lab measurement observations — I have about 80 rows with dates, dataset groups, and both sample_value and sample_weight. Flag any outliers and give me the two analytical reports, please."},{"title":"Multi-group sampling data cleanup","prompt":"I need to clean up my sampling records across two dataset groups: group-a and group-b. There are 45 rows total. Can you normalise the strings and identify outliers in the sample_weight field using IQR analysis?"}],"resultDescription":"Two analytical reports covering: (1) string normalisation results for the specified field across all supplied rows, and (2) IQR-based outlier flags identifying which rows contain extreme values in sample_value or sample_weight, scoped to up to 200 input rows.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, sample_value, or sample_weight) — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","sample_value or sample_weight outside allowed numeric range — rejection","Invalid field selector (not 'sample_value' or 'sample_weight') — enum validation error","Payment not completed — x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 sampling observation records and need both string normalisation and IQR-based outlier detection in a single low-cost call. Ideal for batch data quality checks on survey, lab, or field measurement datasets where you need two complementary analytical perspectives (string hygiene + statistical outlier flagging) without connecting to external systems or live data sources.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:19.013Z","isFirstParty":false,"canonicalSlug":"sampling-observations-string-normalisation-and-iqr-outlier-flags-67ad87f2"}