{"uid":"cap_KNCWL68yTQrEGtNexMzli","slug":"data-freshness-analytics-string-normalisation-iqr-outlier-flags-4d6a66be","name":"Data Freshness Analytics — String Normalisation + IQR Outlier Flags","description":"Data freshness — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied data freshness records (age_minutes, record_count). 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-06-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","age_minutes","record_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"age_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"record_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","age_minutes":10,"record_count":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","age_minutes":20,"record_count":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","age_minutes":15,"record_count":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","age_minutes":30,"record_count":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["age_minutes","record_count"],"type":"string","default":"age_minutes"}}},"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_jW7hyeZPpwWZdUzG1uI0l","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 data freshness records (age_minutes or record_count), returning two independent analytical reports per call.","exampleAgentPrompt":"Can you run string normalisation and IQR outlier flagging on my data freshness records — I have up to 50 rows with age_minutes and dataset_group fields and want two analytical reports back, analysing the age_minutes field?","exampleUseCases":[{"title":"Data pipeline freshness audit","prompt":"I have 80 rows of data freshness logs with row IDs, dataset groups, dates, age_minutes and record counts — can you normalise the string fields and flag any IQR outliers on the age_minutes column so I can see which pipeline feeds are behaving abnormally?"},{"title":"Anomalous record count detection","prompt":"Run IQR outlier detection and string normalisation on these 120 data freshness records — I want to analyse the record_count field and get two reports highlighting which dataset groups have unusually high or low record counts."},{"title":"Pre-reporting data quality check","prompt":"Before I send this data freshness summary to my team, can you clean up the string fields and run IQR-based outlier flags on age_minutes for all 35 rows? I need two independent analytical reports to include in my quality review."}],"resultDescription":"Two independent analytical reports per call covering: (1) string normalisation results for the supplied rows, and (2) IQR-based outlier flags for the chosen numeric field (age_minutes or record_count), applied across up to 200 input rows grouped by dataset_group and date.","failureModes":["More than 200 rows submitted — request rejected with validation error","Missing required fields (row_id, dataset_group, date, age_minutes, record_count) cause row-level rejection","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Null values in the selected numeric field may reduce outlier detection accuracy","Invalid field enum value (not age_minutes or record_count) causes request failure","Empty rows array returns error — minimum 1 row required"],"whenToPreferThis":"Choose this endpoint when you need a combined string normalisation pass and IQR-based outlier detection on data freshness metrics (age_minutes or record_count) for up to 200 rows, all in a single $0.01 call returning two reports. Prefer it over general-purpose stats tools when your data is structured around dataset_group and date freshness records and you want offline, local analytical processing with no external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:19.377Z","isFirstParty":false,"canonicalSlug":"data-freshness-analytics-string-normalisation-iqr-outlier-flags-4d6a66be"}