{"uid":"cap_CtD208nAZ3fqGngyZwoLy","slug":"simulation-caches-record-quality-audit-string-normalisation-explicit-c2a8aa07","name":"Simulation Caches — Record Quality Audit + String Normalisation + Explicit Missing-Value Fill","description":"Simulation caches — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill 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-08-208?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_jhXnRjIoHDSV3ajhbe4eG","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":"Audits record quality, normalises strings, and fills missing values for simulation cache datasets (cache_gb, simulation_seconds) — returning 3 independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you audit the data quality, normalise the strings, and fill missing cache_gb values using median imputation for these simulation cache records — I have about 50 rows with fields like row_id, asset_group, date, cache_gb, and simulation_seconds?","exampleUseCases":[{"title":"Pre-analysis simulation data cleanup","prompt":"I have 120 rows of simulation cache data with asset groups and dates — can you run a full quality audit, normalise any string fields, and fill in any missing cache_gb values using the mean before I start my analysis?"},{"title":"Detecting nulls in simulation seconds","prompt":"Some of my simulation_seconds entries are missing across 80 rows of cache data. Can you audit the record quality, clean up string fields, and fill those gaps using zero imputation so I have a complete dataset?"},{"title":"Routine data hygiene for cache reporting","prompt":"Before I send this batch of 200 simulation cache records to my reporting pipeline, can you do a quality audit, string normalisation, and fill any missing values in the cache_gb column using the median method?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging issues such as nulls, out-of-range values, and structural problems; (2) a string normalisation report showing standardised versions of text fields like asset_group; and (3) an explicit missing-value fill report showing imputed values for the chosen numeric field (cache_gb or simulation_seconds) using the selected method (zero, mean, or median). All results are computed locally with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Invalid date format (not YYYY-MM-DD) causes row rejection","Unsupported fill method enum value causes request failure","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array returns an error","Missing required fields (row_id, asset_group, date) cause schema validation failure"],"whenToPreferThis":"Choose this endpoint when you need three bundled data-cleaning reports — quality audit, string normalisation, and missing-value imputation — specifically for simulation cache records (cache_gb, simulation_seconds) in a single low-cost call. It is ideal for lightweight, offline data hygiene tasks on datasets up to 200 rows where no live lookup, prediction, or external data enrichment is required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:49:15.732Z","isFirstParty":false,"canonicalSlug":"simulation-caches-record-quality-audit-string-normalisation-explicit-c2a8aa07"}