{"uid":"cap_m27RbDHjllfoDx1xg7dq1","slug":"research-task-completion-string-normalisation-explicit-missing-value-32d3c08f","name":"Research Task Completion — String Normalisation + Explicit Missing-Value Fill","description":"Research task completion — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied research task completion records (completed_tasks, assigned_tasks). 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-company-22-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","completed_tasks","assigned_tasks"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"company_group":{"type":"string","default":"","maxLength":80},"assigned_tasks":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"completed_tasks":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","company_group":"group-a","assigned_tasks":1,"completed_tasks":10},{"date":"2026-10-02","row_id":"row-2","company_group":"group-b","assigned_tasks":2,"completed_tasks":20},{"date":"2026-10-03","row_id":"row-3","company_group":"group-a","assigned_tasks":3,"completed_tasks":15},{"date":"2026-10-04","row_id":"row-4","company_group":"group-b","assigned_tasks":4,"completed_tasks":30}],"maxItems":200,"minItems":1},"field":{"enum":["completed_tasks","assigned_tasks"],"type":"string","default":"completed_tasks"},"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_avBWLuXzkLns2DS3D8vu7","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 values in research task completion records (completed_tasks, assigned_tasks) for up to 200 rows, producing 2 analytical reports per call.","exampleAgentPrompt":"I have a batch of research task completion records with some null completed_tasks values and inconsistent company group strings — can you normalise the strings and fill the missing completed_tasks using median imputation for these 4 rows: row-1/group-a/2026-10-01 completed=10 assigned=1, row-2/group-b/2026-10-02 completed=null assigned=2, row-3/group-a/2026-10-03 completed=15 assigned=3, row-4/group-b/2026-10-04 completed=30 assigned=4?","exampleUseCases":[{"title":"Impute missing task counts before reporting","prompt":"I've got 50 rows of weekly task completion data where some completed_tasks entries are null — fill those missing values using the mean and also normalise the company group strings so everything is consistent."},{"title":"Clean assigned task records for a research dashboard","prompt":"Before I load this into our research dashboard, can you normalise the string fields and use median imputation to fill any missing assigned_tasks values across these 120 task completion rows?"},{"title":"Zero-fill nulls in task tracking export","prompt":"I exported our task tracking data and there are gaps in both completed_tasks and assigned_tasks — please normalise the strings and fill all missing values with zero for this batch of 80 records."}],"resultDescription":"Two analytical reports per call: one covering string normalisation outcomes for company_group and other string fields, and one showing explicit missing-value fills for the chosen field (completed_tasks or assigned_tasks) using the selected method (zero, mean, or median), applied across up to 200 supplied rows.","failureModes":["More than 200 rows submitted — request rejected with validation error","Invalid date format (not YYYY-MM-DD) — row fails schema validation","Unknown imputation method value — enum validation error","Empty rows array — minItems:1 violated","Numeric values outside ±1,000,000,000,000 range — schema rejection"],"whenToPreferThis":"Choose this endpoint when you have tabular research task completion records with messy string fields or null numeric values and need both string normalisation and missing-value imputation in a single low-cost call. It operates entirely on supplied data with no external lookups, making it deterministic and safe for sensitive datasets. Best suited for small-to-medium batches (up to 200 rows) where you want two distinct cleaning reports for $0.01.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T18:57:32.610Z","isFirstParty":false,"canonicalSlug":"research-task-completion-string-normalisation-explicit-missing-value-32d3c08f"}