{"uid":"cap_4NrXeXqUfy-WhU9zMKX3_","slug":"discussion-response-string-normalisation-daily-sum-series-e777d46e","name":"Discussion Response — String Normalisation + Daily Sum Series","description":"Discussion response — String normalisation + Daily sum series: String normalisation + Daily sum series for supplied discussion response records (response_count, response_minutes). 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-07-027?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","channel_group","date","response_count","response_minutes"],"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},"response_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"response_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","channel_group":"group-a","response_count":10,"response_minutes":1},{"date":"2026-10-02","row_id":"row-2","channel_group":"group-b","response_count":20,"response_minutes":2},{"date":"2026-10-03","row_id":"row-3","channel_group":"group-a","response_count":15,"response_minutes":3},{"date":"2026-10-04","row_id":"row-4","channel_group":"group-b","response_count":30,"response_minutes":4}],"maxItems":200,"minItems":1},"field":{"enum":["response_count","response_minutes"],"type":"string","default":"response_count"}}},"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_MRNDeUp2lrUSTOsD_ut7T","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":"Runs two local analytical reports on discussion response data: string normalisation of categorical fields and a daily sum time series for response_count or response_minutes (up to 200 rows).","exampleAgentPrompt":"Take these 4 discussion response records — row-1 on 2026-10-01 in group-a with response_count 10 and response_minutes 1, row-2 on 2026-10-02 in group-b with 20 responses and 2 minutes, row-3 on 2026-10-03 in group-a with 15 and 3, row-4 on 2026-10-04 in group-b with 30 and 4 — normalise the string fields and give me a daily sum series for response_count.","exampleUseCases":[{"title":"Daily engagement totals for forum channels","prompt":"I have a week of discussion response data across two channel groups — can you normalise the channel_group strings and compute a daily sum series for response_count? Here are the rows: row-1, 2026-10-01, group-a, 10 responses, 1 minute; row-2, 2026-10-02, group-b, 20 responses, 2 minutes; row-3, 2026-10-03, group-a, 15 responses, 3 minutes."},{"title":"Time-on-thread aggregation by day","prompt":"Can you take these discussion response records and produce a daily sum series for response_minutes? I need to see total minutes spent per day: row-1 on 2026-10-01 group-a 10 responses 5 minutes, row-2 on 2026-10-02 group-b 25 responses 12 minutes, row-3 on 2026-10-03 group-a 8 responses 3 minutes. Also normalise the string fields."},{"title":"Channel label cleanup before reporting","prompt":"My discussion data has inconsistent channel_group labels — please run string normalisation on them and also give me the daily sum series for response_count. Data: row-1, 2026-11-01, Group A, 50 responses, 10 minutes; row-2, 2026-11-01, group-a, 30 responses, 6 minutes; row-3, 2026-11-02, GROUP-B, 40 responses, 8 minutes."}],"resultDescription":"Returns two analytical reports for the supplied rows: (1) a string normalisation report on the categorical/text fields (such as channel_group), and (2) a daily sum time series for the chosen numeric field (response_count or response_minutes), aggregated by date. Results are computed locally with no external lookups.","failureModes":["Empty or missing rows array returns a validation error","rows array exceeding 200 items is rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Null or out-of-range numeric values (beyond ±1,000,000,000,000) may cause computation errors","Missing required fields (row_id, channel_group, date, response_count, response_minutes) on any row returns an error","Invalid field enum value (not response_count or response_minutes) is rejected"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of categorical labels and a daily sum time series computed together in a single call against your own supplied discussion response data, without any external data lookup or prediction. It is ideal for lightweight, self-contained analytical preprocessing of up to 200 discussion response records at very low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T12:37:14.764Z","isFirstParty":false,"canonicalSlug":"discussion-response-string-normalisation-daily-sum-series-e777d46e"}