{"uid":"cap_rRrx1s8nBIlobNGi9QzTc","slug":"video-engagement-string-normalisation-and-exact-row-deduplication-ac7d1f51","name":"Video Engagement String Normalisation and Exact Row Deduplication","description":"Video engagement — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied video engagement records (view_count, watch_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-10-020?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","channel_group","date","view_count","watch_minutes"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"view_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"channel_group":{"type":"string","default":"","maxLength":80},"watch_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","view_count":10,"channel_group":"group-a","watch_minutes":1},{"date":"2026-10-02","row_id":"row-2","view_count":20,"channel_group":"group-b","watch_minutes":2},{"date":"2026-10-03","row_id":"row-3","view_count":15,"channel_group":"group-a","watch_minutes":3},{"date":"2026-10-04","row_id":"row-4","view_count":30,"channel_group":"group-b","watch_minutes":4}],"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_p_cCcDC7K6ZS2hJIpFtLD","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 deduplicates exact rows in video engagement records (view_count, watch_minutes) for up to 200 rows, producing 2 analytical reports locally without external lookups.","exampleAgentPrompt":"Can you clean up these video engagement records for me — normalise the channel_group strings and remove any exact duplicate rows? I have about 50 rows with view_count and watch_minutes fields and I need 2 clean reports back.","exampleUseCases":[{"title":"Pre-pipeline video analytics cleanup","prompt":"I have 120 rows of daily video engagement data with channel groups like 'Group-A', 'group-a', and 'GROUP A' mixed in — can you normalise those strings and strip out any exact duplicate rows before I load this into our data warehouse? I need both reports."},{"title":"YouTube channel deduplication audit","prompt":"We pulled our YouTube watch metrics across 4 channel groups for the past month and the export has some messy string casing and a bunch of duplicate rows — can you normalise the channel_group labels and deduplicate the view_count and watch_minutes records? There are around 80 rows total."},{"title":"Video report data quality check","prompt":"Before I hand off this engagement report to the client, can you run our video data through string normalisation and exact deduplication? It's about 150 rows covering view counts and watch minutes across several channel groups, and I want both output reports to verify the data is clean."}],"resultDescription":"Returns 2 analytical reports based on the supplied video engagement rows: one report for string-normalised records (standardised channel_group and date strings) and one report for exact row deduplication results, identifying and removing fully duplicate rows. All processing is local with no external lookups or predictions.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, channel_group, date, view_count, watch_minutes) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Numeric fields outside allowed range (-1000000000000 to 1000000000000) — rejection","Empty rows array — minimum 1 item required","Payment not provided or insufficient USDC — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local data quality processing on video engagement records specifically (view_count, watch_minutes) without any external API calls, live lookups, or predictions. It is ideal for pre-pipeline cleaning steps where you want both string normalisation and exact deduplication in a single low-cost call for up to 200 rows. Not suitable for fuzzy deduplication, predictive analytics, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T06:48:24.938Z","isFirstParty":false,"canonicalSlug":"video-engagement-string-normalisation-and-exact-row-deduplication-ac7d1f51"}