{"uid":"cap_71Mtky_BFNF_hIAKX6BQ8","slug":"shot-schedules-string-normalisation-and-missing-value-fill-0a8eab61","name":"Shot Schedules String Normalisation and Missing-Value Fill","description":"Shot schedules — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied shot schedules records (planned_hours, actual_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-13-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","planned_hours","actual_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},"actual_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"planned_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","actual_hours":1,"planned_hours":10},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","actual_hours":2,"planned_hours":20},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","actual_hours":3,"planned_hours":15},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","actual_hours":4,"planned_hours":30}],"maxItems":200,"minItems":1},"field":{"enum":["planned_hours","actual_hours"],"type":"string","default":"planned_hours"},"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_CNqFuYRpQzJvEkeFT9YxZ","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 in shot schedule records (planned_hours, actual_hours) for up to 200 rows, producing 2 analytical reports.","exampleAgentPrompt":"I have a shot schedule with some missing planned_hours values — can you clean up the strings and fill in the blanks using median imputation for the planned_hours field? I have about 50 rows covering asset groups like 'group-a' and 'group-b' dated from 2026-10-01 onwards.","exampleUseCases":[{"title":"Fill missing planned hours with median","prompt":"My shot schedule has about 80 rows and several planned_hours entries are null — can you apply median fill to the planned_hours field and normalise all the strings so I get clean, analysis-ready data back?"},{"title":"Zero-fill missing actual hours in production log","prompt":"I've got a production shot log with 120 rows where actual_hours is missing in a bunch of places — please fill those nulls with zero and normalise the string fields so I can import it into my reporting tool."},{"title":"Mean imputation for asset group hours","prompt":"Can you clean my shot schedule records for asset groups 'vfx' and 'lighting' — there are about 40 rows, some actual_hours are blank, and I want them filled using the mean value?"}],"resultDescription":"Returns 2 analytical reports covering the submitted shot schedule rows (up to 200): one covering string normalisation results and one covering explicit missing-value fill for the specified field (planned_hours or actual_hours) using the chosen method (zero, mean, or median). No external lookups or predictions are made; all processing is local to the submitted data.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, asset_group) — validation error","Invalid date format (not YYYY-MM-DD pattern) — schema validation failure","Unsupported fill method or field enum value — request rejected","Payment not received or insufficient USDC — x402 payment required error","Empty rows array — rejected due to minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you have shot schedule data (planned_hours and actual_hours per asset group and date) that contains nulls or inconsistent string formatting and you need both string normalisation and explicit missing-value imputation (zero, mean, or median) in a single call, with results returned as 2 reports. Ideal for pre-processing film or media production schedules before downstream analytics. Not suitable if you need live data lookup, forecasting, or more than 200 rows per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:48:43.702Z","isFirstParty":false,"canonicalSlug":"shot-schedules-string-normalisation-and-missing-value-fill-0a8eab61"}