{"uid":"cap_FbNxHsd06emoF7uWGwDBG","slug":"fact-check-workflow-string-normalisation-explicit-missing-value-fill-53c7884d","name":"Fact Check Workflow — String Normalisation + Explicit Missing-Value Fill","description":"Fact check workflow — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied fact check workflow records (claims_reviewed, references_collected). 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-news-18-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","channel_group","date","claims_reviewed","references_collected"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"channel_group":{"type":"string","default":"","maxLength":80},"claims_reviewed":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"references_collected":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","channel_group":"group-a","claims_reviewed":10,"references_collected":1},{"date":"2026-10-02","row_id":"row-2","channel_group":"group-b","claims_reviewed":20,"references_collected":2},{"date":"2026-10-03","row_id":"row-3","channel_group":"group-a","claims_reviewed":15,"references_collected":3},{"date":"2026-10-04","row_id":"row-4","channel_group":"group-b","claims_reviewed":30,"references_collected":4}],"maxItems":200,"minItems":1},"field":{"enum":["claims_reviewed","references_collected"],"type":"string","default":"claims_reviewed"},"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_A_y2x_8aYuqcxzja58Lvm","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 fact-check workflow records (claims_reviewed, references_collected) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"I have up to 200 rows of fact-check workflow records with channel_group, date, claims_reviewed, and references_collected — can you normalise the strings and fill any missing claims_reviewed values using median imputation, and give me both reports?","exampleUseCases":[{"title":"Impute nulls in claims review dataset","prompt":"I've got a batch of fact-check records where some claims_reviewed values are null — can you fill those missing values using median imputation and also normalise the channel_group strings? Here are my rows with row_id, date, channel_group, claims_reviewed, and references_collected."},{"title":"Clean references data for news analytics pipeline","prompt":"Before I feed my fact-check workflow data into our analytics pipeline, I need to normalise the string fields and fill any missing references_collected values using mean imputation — can you process these rows and give me the two data quality reports?"},{"title":"Zero-fill missing claims for channel group comparison","prompt":"I'm comparing claims reviewed across different channel groups by date, but some entries have null claims_reviewed — please zero-fill those missing values and normalise the strings, and return both analytical reports for my 150 rows."}],"resultDescription":"Two analytical reports covering: (1) string normalisation results for text fields (e.g. channel_group), and (2) explicit missing-value fill results for the selected numeric field (claims_reviewed or references_collected) using the chosen method (zero, mean, or median). No live lookups or external data are used; all processing is local.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, date, channel_group) on any row causes validation error","Invalid date format (non-YYYY-MM-DD) causes schema rejection","Numeric values outside allowed range trigger validation failure","Invalid method enum value (not zero/mean/median) causes error","Invalid field enum value causes error","Empty rows array rejected (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you have tabular fact-check workflow records with messy or missing numeric data (claims_reviewed, references_collected) and need both string normalisation and missing-value imputation in a single low-cost call. Ideal for small-to-medium batches (up to 200 rows) where you need deterministic, offline data cleaning without any live lookups, predictions, or external API calls. Prefer this over general-purpose data cleaning services when your data specifically matches the fact-check workflow schema.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:36:59.340Z","isFirstParty":false,"canonicalSlug":"fact-check-workflow-string-normalisation-explicit-missing-value-fill-53c7884d"}