{"uid":"cap_FEGaoeP357cDxsHGA4FtH","slug":"dataset-storage-string-normalisation-explicit-missing-value-fill-a13adfc1","name":"Dataset Storage — String Normalisation + Explicit Missing-Value Fill","description":"Dataset storage — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied dataset storage records (used_mb, row_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-18-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","used_mb","row_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"used_mb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_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","used_mb":10,"row_count":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","used_mb":20,"row_count":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","used_mb":15,"row_count":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","used_mb":30,"row_count":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["used_mb","row_count"],"type":"string","default":"used_mb"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_j5rB71SFbCMujrbemMeGi","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 fills missing numeric values (used_mb, row_count) in dataset storage records using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 150 dataset storage rows with some null used_mb values and inconsistent dataset_group strings — can you normalise the strings and fill those missing used_mb values using median imputation, then give me two analytical reports?","exampleUseCases":[{"title":"Impute missing storage metrics before reporting","prompt":"I have 80 dataset storage records where some used_mb values are null — fill them using median imputation and normalise the dataset_group strings so I can generate a clean storage report."},{"title":"Clean row_count gaps in pipeline data","prompt":"My dataset storage table has about 60 rows and several row_count fields are missing — can you fill those nulls with the mean and also normalise any messy string fields? I need two summary reports back."},{"title":"Standardise storage records for data warehouse ingestion","prompt":"Before I load these 120 dataset storage rows into our warehouse, I need the dataset_group strings normalised and any null used_mb values filled with zero — run that and return the two analytical reports."}],"resultDescription":"Two analytical reports covering the supplied dataset storage records (up to 200 rows), with string fields normalised and missing numeric values (used_mb or row_count) explicitly filled using the chosen method (zero, mean, or median). No external lookups or predictions are made; all processing uses local analytical engines.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, dataset_group, date) — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure","Invalid field enum value (must be 'used_mb' or 'row_count') — rejected","Invalid method enum value (must be 'zero', 'mean', or 'median') — rejected","Numeric values outside allowed range (±1,000,000,000,000) — validation error","Empty rows array — rejected (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local string normalisation combined with explicit missing-value imputation specifically for dataset storage records (used_mb, row_count fields) and want two analytical reports back in a single low-cost call. Prefer it over general-purpose data cleaning tools when your data fits the dataset storage schema and you need a fast, deterministic, offline result with no external dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:40.156Z","isFirstParty":false,"canonicalSlug":"dataset-storage-string-normalisation-explicit-missing-value-fill-a13adfc1"}