{"uid":"cap_zeHgqsf3PrRu9DCbhLfWF","slug":"correction-logs-string-normalisation-numeric-interval-filter-3d9b219a","name":"Correction Logs String Normalisation + Numeric Interval Filter","description":"Correction logs — String normalisation + Numeric interval filter: String normalisation + Numeric interval filter for supplied correction logs records (correction_count, published_items). 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-04-026?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","channel_group","date","correction_count","published_items"],"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},"published_items":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"correction_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","channel_group":"group-a","published_items":1,"correction_count":10},{"date":"2026-10-02","row_id":"row-2","channel_group":"group-b","published_items":2,"correction_count":20},{"date":"2026-10-03","row_id":"row-3","channel_group":"group-a","published_items":3,"correction_count":15},{"date":"2026-10-04","row_id":"row-4","channel_group":"group-b","published_items":4,"correction_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["correction_count","published_items"],"type":"string","default":"correction_count"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_CqsmryP6pAURHwyC0Oq3W","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 filters correction log rows by a numeric interval (min/max) for correction_count or published_items, delivering two analytical reports from supplied data.","exampleAgentPrompt":"I have up to 200 rows of correction log records with fields like channel_group, date, correction_count, and published_items — can you normalise the string fields and filter the rows to only keep those where correction_count is between 5 and 100, and give me the two analytical reports?","exampleUseCases":[{"title":"Editorial correction rate range audit","prompt":"Here are my newsroom correction logs for October — can you normalise the channel_group strings and filter down to only rows where correction_count is between 10 and 50, then give me the two reports on what's left?"},{"title":"Published items volume band filter","prompt":"I've got 150 rows of correction log data with published_items counts — please normalise the string fields and filter to rows where published_items falls between 100 and 5000, using the published_items field for the interval check."},{"title":"Low-correction channel identification","prompt":"Can you take my correction logs dataset, normalise channel_group strings, and filter to only rows with correction_count between 0 and 5 so I can identify channels with very few corrections? I need the two analytical reports back."}],"resultDescription":"Two independent analytical reports based on the supplied correction log rows: one reflecting string-normalised channel_group values and one showing only rows whose selected numeric field (correction_count or published_items) falls within the specified minimum–maximum interval. Up to 200 rows processed per call with no external lookups.","failureModes":["Rows array empty or missing — request rejected","More than 200 rows submitted — exceeds maximum item limit","Invalid date format (non YYYY-MM-DD) — schema validation failure","Numeric values outside ±1,000,000,000,000 range — schema rejection","Field enum value not 'correction_count' or 'published_items' — validation error","Minimum greater than maximum — may return zero filtered rows","Missing required row fields (row_id, channel_group, date) — schema error"],"whenToPreferThis":"Choose this endpoint when you have tabular correction log records (up to 200 rows) that need string field normalisation combined with numeric range filtering on either correction_count or published_items, and you want two bundled analytical reports in a single low-cost call. Prefer it over general-purpose data tools when your data fits the news/editorial correction domain schema exactly and you want a lightweight, local analytical pass without external API round-trips or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:39:24.681Z","isFirstParty":false,"canonicalSlug":"correction-logs-string-normalisation-numeric-interval-filter-3d9b219a"}