{"uid":"cap_13opMHJ3F_Vn6q4nPhYak","slug":"save-files-string-normalisation-exact-row-deduplication-10ecfd34","name":"Save Files — String Normalisation + Exact Row Deduplication","description":"Save files — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied save files records (save_kb, write_ms). 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-game-13-020?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","game_area","date","save_kb","write_ms"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"save_kb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"write_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"game_area":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","save_kb":10,"write_ms":1,"game_area":"group-a"},{"date":"2026-10-02","row_id":"row-2","save_kb":20,"write_ms":2,"game_area":"group-b"},{"date":"2026-10-03","row_id":"row-3","save_kb":15,"write_ms":3,"game_area":"group-a"},{"date":"2026-10-04","row_id":"row-4","save_kb":30,"write_ms":4,"game_area":"group-b"}],"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_aa6jXC2fT8a2XwRdUJva9","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 video game save file records (save_kb, write_ms), producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you clean up my game save file records by normalising the string fields and removing exact duplicate rows? I have up to 200 rows with fields like row_id, game_area, date, save_kb, and write_ms — I need two reports back covering string normalisation and deduplication.","exampleUseCases":[{"title":"Game save data deduplication audit","prompt":"I have a batch of game save records with columns row_id, game_area, date, save_kb, and write_ms — some rows look like exact duplicates. Can you run string normalisation and exact deduplication on them and give me two reports?"},{"title":"Pre-analysis save file cleanup","prompt":"Before I analyse my game save logs, I need to normalise the text fields and strip out any exact duplicate rows. The data has up to 150 entries with row_id, game_area, date, save_kb, and write_ms. Can you do that and return the two cleaned reports?"},{"title":"Save file quality check by game area","prompt":"Can you take my save file dataset — it has row_id, game_area, date, save_kb in KB, and write_ms in milliseconds — and produce a string normalisation report plus an exact row deduplication report? There are about 80 rows across different game areas."}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report covering text fields such as row_id and game_area, and (2) an exact row deduplication report identifying and removing fully identical rows. Both reports are computed locally from the supplied data, with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, game_area, date, save_kb, write_ms) cause validation errors","Date field not in YYYY-MM-DD format causes schema rejection","Numeric fields (save_kb, write_ms) out of allowed range cause errors","Empty rows array (minItems: 1 not met) causes rejection"],"whenToPreferThis":"Choose this endpoint when you have video game save file records needing both string field normalisation and exact duplicate row removal in a single call, and your dataset is 200 rows or fewer. It is ideal for lightweight, offline data quality checks on save file metrics (save_kb, write_ms) without requiring live lookups or external data enrichment. Prefer it over general-purpose deduplication tools when you specifically need two co-produced reports (normalisation + deduplication) at low cost ($0.01 USDC).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T19:07:34.443Z","isFirstParty":false,"canonicalSlug":"save-files-string-normalisation-exact-row-deduplication-10ecfd34"}