{"uid":"cap_0HgfH_XRAZELwIQxdbWrE","slug":"simulation-caches-string-normalisation-adjacent-row-changes-4fc4e068","name":"Simulation Caches — String Normalisation + Adjacent Row Changes","description":"Simulation caches — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied simulation caches records (cache_gb, simulation_seconds). 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-08-029?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","cache_gb","simulation_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"cache_gb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"asset_group":{"type":"string","default":"","maxLength":80},"simulation_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","cache_gb":10,"asset_group":"group-a","simulation_seconds":1},{"date":"2026-10-02","row_id":"row-2","cache_gb":20,"asset_group":"group-b","simulation_seconds":2},{"date":"2026-10-03","row_id":"row-3","cache_gb":15,"asset_group":"group-a","simulation_seconds":3},{"date":"2026-10-04","row_id":"row-4","cache_gb":30,"asset_group":"group-b","simulation_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["cache_gb","simulation_seconds"],"type":"string","default":"cache_gb"}}},"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_XD63jyYnXz-GSapBMEvEW","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":"Runs two local analytical reports on simulation cache records: string normalisation and adjacent row change detection, for up to 200 rows of cache_gb or simulation_seconds data.","exampleAgentPrompt":"Can you run string normalisation and adjacent row change detection on my simulation cache records? I have 4 rows with dates from 2026-10-01 to 2026-10-04, asset groups group-a and group-b, and I want to analyse the cache_gb field.","exampleUseCases":[{"title":"Cache growth anomaly detection","prompt":"I have a week of simulation cache logs with daily cache_gb readings across two asset groups — can you check for any sudden jumps or drops between consecutive rows and also normalise the asset group labels?"},{"title":"Simulation runtime drift analysis","prompt":"Here are 50 rows of simulation records with simulation_seconds values per date and asset group — run adjacent row change detection on simulation_seconds so I can see where runtimes shifted significantly between days."},{"title":"Data cleanup before pipeline ingestion","prompt":"Before I load these 120 simulation cache rows into our pipeline, can you normalise the string fields and flag any unusual adjacent changes in cache_gb so I can review them first?"}],"resultDescription":"Two analytical reports per call: (1) string normalisation results for the specified field across all supplied rows, and (2) adjacent row change detection showing deltas between consecutive rows — both computed locally without external lookups, covering up to 200 input rows.","failureModes":["More than 200 rows submitted — request rejected at maxItems validation","Missing required fields (row_id, date, asset_group, cache_gb, simulation_seconds) cause schema validation failure","Invalid date format (not YYYY-MM-DD) causes pattern validation failure","field enum value outside [cache_gb, simulation_seconds] causes validation error","Null numeric values may produce incomplete change detection output for affected rows"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline string normalisation and sequential change detection on simulation cache data (cache_gb or simulation_seconds) without any external API calls or live data lookups — ideal for pre-pipeline data hygiene checks on batches of up to 200 rows at low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:49:09.241Z","isFirstParty":false,"canonicalSlug":"simulation-caches-string-normalisation-adjacent-row-changes-4fc4e068"}