{"uid":"cap_yYTHs0Wi6sOXQo32GR41c","slug":"simulation-caches-string-normalisation-iqr-outlier-flags-bfdaa2cf","name":"Simulation Caches — String Normalisation + IQR Outlier Flags","description":"Simulation caches — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags 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-08-032?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_11WmtsJzJ0ThNUPQQagxf","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 string fields and flags IQR-based outliers in simulation cache records (cache_gb and simulation_seconds) for up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"Can you analyse my simulation cache records for string normalisation issues and IQR outlier flags? I have 4 rows — row-1 on 2026-10-01 for group-a with cache_gb=10 and simulation_seconds=1, row-2 on 2026-10-02 for group-b with cache_gb=20 and simulation_seconds=2, row-3 on 2026-10-03 for group-a with cache_gb=15 and simulation_seconds=3, and row-4 on 2026-10-04 for group-b with cache_gb=30 and simulation_seconds=4 — run it on the cache_gb field.","exampleUseCases":[{"title":"Outlier detection in simulation cache logs","prompt":"I have a batch of simulation cache records from last quarter — can you flag any IQR outliers in the cache_gb values and also normalise the asset_group strings? Here are my rows: row-1, 2026-10-01, group-a, cache_gb=10, simulation_seconds=60; row-2, 2026-10-02, group-B, cache_gb=500, simulation_seconds=120; row-3, 2026-10-03, group-a, cache_gb=12, simulation_seconds=90. Run the analysis on cache_gb."},{"title":"Quality check on simulation run durations","prompt":"Can you run string normalisation and IQR outlier detection on simulation_seconds for these simulation cache rows? row-1 on 2026-11-01, group-alpha, cache_gb=5, simulation_seconds=300; row-2 on 2026-11-02, group-Alpha, cache_gb=8, simulation_seconds=9999; row-3 on 2026-11-03, group-alpha, cache_gb=6, simulation_seconds=310 — use simulation_seconds as the target field."},{"title":"Asset group name normalisation with cache anomaly check","prompt":"I've got messy asset_group labels mixed in with my simulation cache data and I want to see which cache_gb entries are statistical outliers. Can you process these 5 rows — row-1 2026-09-01 Group-A cache_gb=100 simulation_seconds=10, row-2 2026-09-02 group-a cache_gb=105 simulation_seconds=11, row-3 2026-09-03 GROUP-A cache_gb=102 simulation_seconds=9, row-4 2026-09-04 group-b cache_gb=800 simulation_seconds=12, row-5 2026-09-05 Group-B cache_gb=98 simulation_seconds=10 — target field cache_gb?"}],"resultDescription":"Returns two independent analytical reports per call: (1) a string normalisation report covering the asset_group and date fields, and (2) IQR outlier flags for the specified numeric field (cache_gb or simulation_seconds), identifying which rows fall outside the interquartile range bounds. No external lookups or predictions are made; all analysis is performed locally on the supplied data.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds maximum row limit","Invalid date format (non-YYYY-MM-DD) — validation error","Numeric values outside allowed range (±1,000,000,000,000) — schema violation","Missing required fields (row_id, asset_group, date, cache_gb, simulation_seconds) — request rejected","Invalid field enum value (not cache_gb or simulation_seconds) — schema error","Insufficient data variance for meaningful IQR calculation on very small datasets"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and IQR-based outlier detection on simulation cache records in a single low-cost call. Ideal for data quality pipelines dealing with simulation_seconds and cache_gb metrics across asset groups, where you want statistical anomaly detection without external model inference or live data lookups. Best suited for batch sizes up to 200 rows where local analytical computation is sufficient.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T18:56:43.639Z","isFirstParty":false,"canonicalSlug":"simulation-caches-string-normalisation-iqr-outlier-flags-bfdaa2cf"}