{"uid":"cap__3crpZSyL4VY7lt1CHL2Q","slug":"image-exports-string-normalisation-exact-row-deduplication-dab342fa","name":"Image Exports — String Normalisation + Exact Row Deduplication","description":"Image exports — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied image exports records (width_pixels, height_pixels). 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-creative-17-020?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","width_pixels","height_pixels"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"asset_group":{"type":"string","default":"","maxLength":80},"width_pixels":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"height_pixels":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","width_pixels":10,"height_pixels":1},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","width_pixels":20,"height_pixels":2},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","width_pixels":15,"height_pixels":3},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","width_pixels":30,"height_pixels":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__RJsvnO36ohoAabdAWWLe","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 removes exact duplicate rows from image export records containing width and height pixel dimensions, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you clean up my image export records by normalising the strings and removing any exact duplicate rows? I have up to 200 rows with fields like row_id, asset_group, date, width_pixels, and height_pixels — I need two reports back.","exampleUseCases":[{"title":"Deduplicating image asset export pipeline","prompt":"I have a batch of image export records from our creative team with fields for row_id, asset_group, date, width_pixels, and height_pixels — some rows are exact duplicates. Can you run string normalisation and exact deduplication on them and give me the two reports?"},{"title":"Cleaning creative asset dimension data","prompt":"Our image export dataset has inconsistent string formatting in the asset_group field and some exact duplicate rows. Run normalisation and deduplication on these records — there are about 150 rows with width and height pixel values — and give me both analytical reports."},{"title":"Pre-analysis image record quality pass","prompt":"Before we do any analysis on our image exports data, I want to normalise all the text fields and deduplicate any identical rows. The dataset has row_id, asset_group, date, width_pixels, and height_pixels columns with around 80 rows — can you generate the two cleaning reports?"}],"resultDescription":"Returns two analytical reports: one covering string normalisation applied to the image export records (e.g. trimming, casing, whitespace standardisation on text fields like asset_group), and one covering exact row deduplication results identifying and removing fully identical rows from the supplied dataset of up to 200 image export rows.","failureModes":["Exceeding 200 rows results in rejection or truncation","Missing required fields (row_id, date, width_pixels, height_pixels) causes validation failure","Invalid date format (not YYYY-MM-DD) triggers schema error","Submitting zero rows violates minItems constraint","Pixel values outside the allowed numeric range cause schema rejection"],"whenToPreferThis":"Choose this endpoint when you have image export records with width and height pixel dimensions that need both string normalisation and exact row deduplication in a single call. It is ideal for pre-processing creative asset data before downstream analytics when you need two independent cleaning reports without any live lookups or external integrations. Prefer this over general-purpose deduplication tools when your data specifically involves image export dimensions organised by asset group and date.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:32:33.429Z","isFirstParty":false,"canonicalSlug":"image-exports-string-normalisation-exact-row-deduplication-dab342fa"}