{"uid":"cap_XAmOaRo9oHEBrE3krcbVM","slug":"simulation-caches-record-quality-audit-string-normalisation-daily-sum-1d3858a1","name":"Simulation Caches — Record Quality Audit + String Normalisation + Daily Sum Series","description":"Simulation caches — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum series for supplied simulation caches records (cache_gb, simulation_seconds). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-creative-08-198?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_Ty8T_u5FGTXEzu7i_tHwg","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 three local analytical reports on simulation cache records: record quality audit, string normalisation, and daily sum series aggregation — up to 200 rows per call.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and daily sum series on my simulation cache data? I have rows with asset groups like 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and cache_gb values — use cache_gb as the numeric field.","exampleUseCases":[{"title":"Daily cache usage totals by date","prompt":"I have a list of simulation cache records with dates, asset groups, and cache_gb values — can you generate a daily sum series so I can see total cache usage per day? Use cache_gb as the field and here are my rows: row-1 on 2026-10-01, group-a, 10 GB; row-2 on 2026-10-02, group-b, 20 GB; row-3 on 2026-10-03, group-a, 15 GB."},{"title":"Audit simulation record data quality","prompt":"Can you run a record quality audit on these simulation cache records to flag any missing values, out-of-range numbers, or malformed dates? I've got about 50 rows with row_id, asset_group, date, cache_gb, and simulation_seconds fields."},{"title":"Normalise asset group labels across simulation data","prompt":"My simulation cache dataset has inconsistent asset group names — things like 'Group A', 'group-a', and 'GROUP_A' all meaning the same thing. Can you run the string normalisation report on my rows so I get a clean, consistent version of each asset_group label?"}],"resultDescription":"Returns three independent analytical reports for the submitted rows: (1) a record quality audit flagging data issues such as nulls or out-of-range values, (2) a string normalisation report cleaning the asset_group text field, and (3) a daily sum series aggregating the chosen numeric field (cache_gb or simulation_seconds) by date. All processing is local with no external lookups.","failureModes":["More than 200 rows submitted — endpoint rejects or truncates input","Invalid date format (not YYYY-MM-DD) causes row-level errors","Missing required fields (row_id, asset_group, date) causes validation failure","Invalid field enum value (not 'cache_gb' or 'simulation_seconds') causes request rejection","Empty rows array returns an error","Payment of 0.01 USDC not included or declined results in 402 response"],"whenToPreferThis":"Choose this endpoint when you need three specific analytical operations — quality audit, string normalisation, and daily sum series — performed together on simulation cache data (cache_gb, simulation_seconds) in a single cost-effective call. It is ideal for batch sizes up to 200 rows where no live external data, predictions, or professional assessments are required, and all computation is self-contained.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T18:57:00.949Z","isFirstParty":false,"canonicalSlug":"simulation-caches-record-quality-audit-string-normalisation-daily-sum-1d3858a1"}