{"uid":"cap_fqmQvB1wgw9HEgBUh3duP","slug":"validator-uptime-string-normalisation-iqr-outlier-flags-82de901c","name":"Validator Uptime — String Normalisation + IQR Outlier Flags","description":"Validator uptime — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied validator uptime records (online_minutes, observed_minutes). 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-finance-12-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","instrument_group","date","online_minutes","observed_minutes"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"online_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"instrument_group":{"type":"string","default":"","maxLength":80},"observed_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","online_minutes":10,"instrument_group":"group-a","observed_minutes":1},{"date":"2026-10-02","row_id":"row-2","online_minutes":20,"instrument_group":"group-b","observed_minutes":2},{"date":"2026-10-03","row_id":"row-3","online_minutes":15,"instrument_group":"group-a","observed_minutes":3},{"date":"2026-10-04","row_id":"row-4","online_minutes":30,"instrument_group":"group-b","observed_minutes":4}],"maxItems":200,"minItems":1},"field":{"enum":["online_minutes","observed_minutes"],"type":"string","default":"online_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_wKBMjCNa2ibXIgY15GfnC","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 validator uptime records (online_minutes, observed_minutes) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"I have a batch of validator uptime records with online_minutes and observed_minutes fields — can you run string normalisation and IQR outlier detection on the online_minutes field across these rows and give me 2 reports flagging anything unusual?","exampleUseCases":[{"title":"Staking pool uptime anomaly audit","prompt":"Here are 150 rows of daily validator uptime logs with online_minutes and observed_minutes. Can you normalise the string fields and flag any IQR outliers in online_minutes so I can spot validators that were behaving unusually last month?"},{"title":"Observed minutes data quality check","prompt":"I've got a CSV of validator observations and I want to check the observed_minutes column for outliers using IQR analysis — can you run string normalisation and generate the two outlier-flag reports on this batch of around 80 rows?"},{"title":"Pre-report data cleaning for validator dashboard","prompt":"Before I publish our quarterly validator performance report, can you clean up string inconsistencies and run IQR outlier flags on both online_minutes for these 200 validator records so I can exclude bad data points?"}],"resultDescription":"Two analytical reports per call: (1) a string normalisation report summarising cleaned/standardised string fields across the submitted rows, and (2) an IQR outlier flag report identifying which rows contain values in online_minutes or observed_minutes that fall outside the interquartile range, with row-level flags and summary statistics. Covers up to 200 input rows.","failureModes":["Fewer than 1 or more than 200 rows submitted — validation error","Missing required fields (row_id, date, online_minutes, observed_minutes, instrument_group) — schema rejection","Date not in YYYY-MM-DD format — pattern validation failure","Insufficient data spread for IQR calculation (e.g. all identical values) — may return empty outlier flags","Invalid field enum value (not online_minutes or observed_minutes) — schema error","Payment not completed via x402 — request blocked"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of validator uptime data and need both string normalisation and IQR-based outlier detection in a single low-cost call. It is purpose-built for validator uptime records (online_minutes, observed_minutes) and returns two independent analytical reports. Prefer this over generic data tools when your dataset fits the validator/staking domain and you want structured outlier flags without building your own statistical pipeline.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:33:46.468Z","isFirstParty":false,"canonicalSlug":"validator-uptime-string-normalisation-iqr-outlier-flags-82de901c"}