{"uid":"cap_whrBBqx86RhiIZXRvae8T","slug":"newsletter-observations-string-normalisation-exact-row-deduplication-fa075c62","name":"Newsletter Observations — String Normalisation + Exact Row Deduplication","description":"Newsletter observations — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied newsletter observations records (delivered_messages, open_count). 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-08-020?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","channel_group","date","delivered_messages","open_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"open_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"channel_group":{"type":"string","default":"","maxLength":80},"delivered_messages":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","open_count":1,"channel_group":"group-a","delivered_messages":10},{"date":"2026-10-02","row_id":"row-2","open_count":2,"channel_group":"group-b","delivered_messages":20},{"date":"2026-10-03","row_id":"row-3","open_count":3,"channel_group":"group-a","delivered_messages":15},{"date":"2026-10-04","row_id":"row-4","open_count":4,"channel_group":"group-b","delivered_messages":30}],"maxItems":200,"minItems":1}}},"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_EiL4YrM9rRFT2jG3fOdy7","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":"Cleans and deduplicates up to 200 newsletter observation records (delivered_messages, open_count) via string normalisation and exact row deduplication, returning 2 analytical reports.","exampleAgentPrompt":"Can you clean up my newsletter data — normalise the channel group strings and remove exact duplicate rows? I have records with fields like row_id, channel_group, date, delivered_messages, and open_count, about 50 rows total.","exampleUseCases":[{"title":"Deduplicating newsletter campaign exports","prompt":"I exported our weekly newsletter stats and suspect there are duplicate rows. Can you normalise the string fields and strip out exact duplicates from this dataset? It has channel_group, date, delivered_messages, and open_count columns, around 120 rows."},{"title":"Standardising multi-source newsletter aggregation","prompt":"We're merging newsletter observations from two systems and the channel group names have inconsistent capitalisation and spacing. Can you run string normalisation and exact deduplication on the combined records — about 180 rows with delivered_messages and open_count per row?"},{"title":"Pre-analysis data quality pass on open rate data","prompt":"Before I calculate open rates, I need to make sure my 90-row newsletter dataset is clean — no duplicate entries and all string fields normalised. The rows have row_id, channel_group, date, delivered_messages, and open_count. Can you run the deduplication and normalisation reports?"}],"resultDescription":"Returns 2 analytical reports covering (1) string normalisation outcomes for text fields like channel_group and (2) exact row deduplication results identifying and removing duplicate records across the supplied newsletter observation rows. No external lookups or predictions are made; all processing is local and analytical.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, channel_group, date, delivered_messages, open_count) cause validation errors","Invalid date format (not YYYY-MM-DD) may cause parsing failures","Numeric fields outside allowed range (-1e12 to 1e12) rejected","Empty rows array returns an error (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) batch cleaning pass on newsletter observation data — specifically for string normalisation of categorical fields and exact duplicate row removal. Ideal for pre-processing newsletter exports before aggregation or reporting, when datasets are 200 rows or fewer and no live enrichment or prediction is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T12:45:29.733Z","isFirstParty":false,"canonicalSlug":"newsletter-observations-string-normalisation-exact-row-deduplication-fa075c62"}