{"uid":"cap_c3CiM6B9VOC0TwwDPtcqT","slug":"company-record-completeness-string-normalisation-missing-value-fill-6b522749","name":"Company Record Completeness — String Normalisation & Missing-Value Fill","description":"Company record completeness — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied company record completeness records (populated_fields, required_fields). 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-01-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","populated_fields","required_fields"],"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},"required_fields":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_fields":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","company_group":"group-a","required_fields":1,"populated_fields":10},{"date":"2026-10-02","row_id":"row-2","company_group":"group-b","required_fields":2,"populated_fields":20},{"date":"2026-10-03","row_id":"row-3","company_group":"group-a","required_fields":3,"populated_fields":15},{"date":"2026-10-04","row_id":"row-4","company_group":"group-b","required_fields":4,"populated_fields":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_fields","required_fields"],"type":"string","default":"populated_fields"},"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_jOl8lwz87nZKBaCESxi5i","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 explicit missing values in company record completeness data (populated_fields, required_fields) for up to 200 rows, producing 2 analytical reports.","exampleAgentPrompt":"I have 150 rows of company record completeness data with some missing populated_fields values — can you run string normalisation and fill those missing values using median imputation, grouping by company_group and date, and give me the two analytical reports?","exampleUseCases":[{"title":"Median fill for incomplete CRM records","prompt":"I have a batch of 80 company CRM completeness rows where some populated_fields entries are null — please fill the missing values using median and normalise the strings so I can get the two reports back."},{"title":"Zero-fill for required fields audit","prompt":"I'm auditing company record completeness across our groups. I have 200 rows with dates from 2026-Q1, and I need any missing required_fields values filled with zero and the strings normalised — run both analytical reports on the supplied data."},{"title":"Mean imputation before dashboard refresh","prompt":"Before we refresh our data quality dashboard, can you take these 60 company completeness rows, normalise the string fields, and fill any missing populated_fields using mean imputation? I need both reports generated from the cleaned data."}],"resultDescription":"Returns two independent analytical reports on the supplied company record completeness data, with string fields normalised and explicit missing values in the chosen field (populated_fields or required_fields) filled using the selected method (zero, mean, or median). No live lookups or external data are used — analysis is performed locally on the submitted rows.","failureModes":["Fewer than 1 or more than 200 rows submitted — rejected by schema validation","Invalid date format (not YYYY-MM-DD) — validation error","populated_fields or required_fields values outside allowed numeric range — validation error","Unknown fill method or field enum value — schema rejection","Empty or missing row_id — rejected by minLength constraint"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local string normalisation and missing-value imputation specifically for company record completeness metrics (populated_fields / required_fields) in batches up to 200 rows and want two bundled analytical reports for a flat $0.01 USDC fee. Prefer it over general-purpose ETL tools when you need a quick, cost-effective offline data-cleaning pass without live lookups or external enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:19.562Z","isFirstParty":false,"canonicalSlug":"company-record-completeness-string-normalisation-missing-value-fill-6b522749"}