{"uid":"cap_5m3oEXy5H1wZwqgIL9ONN","slug":"asset-revisions-string-normalisation-and-missing-value-fill-cc88a3e7","name":"Asset Revisions String Normalisation and Missing-Value Fill","description":"Asset revisions — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied asset revisions records (revision_count, rework_hours). 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-creative-14-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","revision_count","rework_hours"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"asset_group":{"type":"string","default":"","maxLength":80},"rework_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"revision_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","rework_hours":1,"revision_count":10},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","rework_hours":2,"revision_count":20},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","rework_hours":3,"revision_count":15},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","rework_hours":4,"revision_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["revision_count","rework_hours"],"type":"string","default":"revision_count"},"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_ZS0QBFQMS5VArDQLrIr3K","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 strings and fills missing values for asset revision records (revision_count, rework_hours) across up to 200 rows, producing 2 analytical reports.","exampleAgentPrompt":"Can you clean up this asset revision dataset — normalise the strings and fill any missing revision_count values using median imputation? I have 4 rows covering asset groups A and B from October 2026.","exampleUseCases":[{"title":"Impute missing rework hours for reporting","prompt":"I have a dataset of asset revision records with some null rework_hours entries — can you fill those missing values using mean imputation and also normalise the string fields? Here are 10 rows from group-a and group-b across Q4."},{"title":"Zero-fill missing revision counts before dashboard load","prompt":"Before I load my asset revision data into the dashboard, can you fill any missing revision_count values with zero and normalise the strings across all 50 rows I'm sending?"},{"title":"Clean asset group data for audit submission","prompt":"I need to prepare my asset revision records for an audit — please normalise the text fields and use median fill to handle any null revision_count or rework_hours values in these 120 rows spanning multiple asset groups."}],"resultDescription":"Returns 2 reports covering the submitted asset revision rows: one covering string normalisation (standardised text fields) and one covering explicit missing-value fill for the chosen field (revision_count or rework_hours) using the selected method (zero, mean, or median). No external data is fetched; all processing is performed locally on the supplied rows.","failureModes":["Rows array empty or exceeds 200-item limit — validation error returned","Invalid date format (not YYYY-MM-DD) causes row rejection","Unknown fill method enum value causes request failure","Numeric values outside allowed range rejected","Missing required fields (row_id, asset_group, date) cause per-row errors"],"whenToPreferThis":"Choose this endpoint when you need a low-cost ($0.01 USDC for 2 reports), offline, deterministic data-cleaning step specifically for asset revision records with revision_count and rework_hours fields. It is ideal when you want both string normalisation and missing-value imputation in a single call, without any live data lookup, ML prediction, or external side effects. Best suited for batch sizes up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T12:43:55.755Z","isFirstParty":false,"canonicalSlug":"asset-revisions-string-normalisation-and-missing-value-fill-cc88a3e7"}