{"uid":"cap__lyheDgCbv48OmEO2JR68","slug":"loot-observations-string-normalisation-adjacent-row-changes-64908f1b","name":"Loot Observations — String Normalisation + Adjacent Row Changes","description":"Loot observations — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied loot observations records (drop_count, attempt_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-game-05-029?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","game_area","date","drop_count","attempt_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"game_area":{"type":"string","default":"","maxLength":80},"drop_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"attempt_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","game_area":"group-a","drop_count":10,"attempt_count":1},{"date":"2026-10-02","row_id":"row-2","game_area":"group-b","drop_count":20,"attempt_count":2},{"date":"2026-10-03","row_id":"row-3","game_area":"group-a","drop_count":15,"attempt_count":3},{"date":"2026-10-04","row_id":"row-4","game_area":"group-b","drop_count":30,"attempt_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["drop_count","attempt_count"],"type":"string","default":"drop_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__felQE6KnpnAVgJdSEbzE","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 game_area strings and computes adjacent row changes for loot observation records (drop_count or attempt_count) across up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"Can you normalise the game_area strings and calculate row-over-row changes in drop_count for these loot records? I have 4 rows: row-1 on 2026-10-01 in group-a with drop_count 10 and attempt_count 1, row-2 on 2026-10-02 in group-b with drop_count 20 and attempt_count 2, row-3 on 2026-10-03 in group-a with drop_count 15 and attempt_count 3, and row-4 on 2026-10-04 in group-b with drop_count 30 and attempt_count 4.","exampleUseCases":[{"title":"Daily loot drop delta tracking","prompt":"I have daily loot data for two game areas over the past week — can you normalise the area names and show me how drop_count changed from one day to the next? Here are 7 rows with row IDs row-1 through row-7, dates 2026-10-01 to 2026-10-07, alternating game areas 'Zone A' and 'zone-a', and drop counts 5, 12, 9, 20, 18, 25, 30, all with attempt_count 1."},{"title":"Attempt count trend analysis for game sessions","prompt":"Can you run string normalisation and adjacent row changes on my attempt_count field for these loot observation records? I've got 6 rows: row-1 through row-6, dates 2026-11-01 to 2026-11-06, all in game_area 'dungeon-1', attempt_counts 3, 7, 5, 12, 10, 15, and drop_counts all set to 0."},{"title":"Multi-area loot consistency audit","prompt":"I need to clean up inconsistent game area labels and find row-to-row swings in drop_count across these 10 loot observation rows spanning two areas called 'Boss Arena' and 'boss_arena' — dates run from 2026-09-01 to 2026-09-10, row IDs row-1 to row-10, drop_counts ranging 50 to 200, and attempt_counts 5 to 20."}],"resultDescription":"Returns 2 independent analytical reports: one covering string normalisation of the game_area field across all submitted rows, and one showing adjacent (row-to-row) changes in the selected numeric field (drop_count or attempt_count). No external lookups or predictions are included.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, game_area, date, drop_count, attempt_count) cause rejection","Invalid date format (not YYYY-MM-DD) triggers a pattern validation error","Numeric values outside ±1,000,000,000,000 are rejected","Invalid field enum value (anything other than drop_count or attempt_count) causes an error","Payment failure or insufficient USDC balance prevents processing"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of categorical game area labels and adjacent row change computation on loot metrics in a single low-cost call. It is ideal for game analytics pipelines processing sequential loot observation data without needing live lookups, external enrichment, or predictions. At $0.01 USDC for 2 reports on up to 200 rows, it is cost-effective for batch offline analysis of game session logs.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T12:37:19.941Z","isFirstParty":false,"canonicalSlug":"loot-observations-string-normalisation-adjacent-row-changes-64908f1b"}