{"uid":"cap_-St1TWn-0anQqTM_eDncL","slug":"controller-sampling-string-normalisation-two-measure-correlation-0f8d97a4","name":"Controller Sampling — String Normalisation + Two-Measure Correlation","description":"Controller sampling — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied controller sampling records (input_latency_ms, sample_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-15-033?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","game_area","date","input_latency_ms","sample_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},"sample_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"input_latency_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","game_area":"group-a","sample_count":1,"input_latency_ms":10},{"date":"2026-10-02","row_id":"row-2","game_area":"group-b","sample_count":2,"input_latency_ms":20},{"date":"2026-10-03","row_id":"row-3","game_area":"group-a","sample_count":3,"input_latency_ms":15},{"date":"2026-10-04","row_id":"row-4","game_area":"group-b","sample_count":4,"input_latency_ms":30}],"maxItems":200,"minItems":1}}},"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_9GtF0bwtlStU6f4wiv9td","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 computes correlation between input_latency_ms and sample_count across up to 200 controller sampling records, producing two analytical reports.","exampleAgentPrompt":"Can you run string normalisation and two-measure correlation on my controller sampling data? I have up to 50 rows with fields like row_id, game_area, date, input_latency_ms, and sample_count — I want both reports showing how latency and sample count relate.","exampleUseCases":[{"title":"Game area latency correlation audit","prompt":"I have controller sampling records from our game — rows with game_area labels like 'group-a' and 'group-b', dates in 2026, and columns for input_latency_ms and sample_count. Can you normalise the string fields and compute a two-measure correlation between latency and sample count for me? There are about 80 rows."},{"title":"QA pipeline string cleanup and stats","prompt":"Our QA team exported 120 controller sampling rows with messy game_area strings and numeric columns for input latency in milliseconds and sample count. Please normalise those strings and give me the two-measure correlation report between input_latency_ms and sample_count."},{"title":"Daily controller performance statistical check","prompt":"Here are today's 40 controller sampling records with row IDs, dates, game areas, input latency readings, and sample counts. Run string normalisation on the text fields and produce both correlation reports comparing the latency and sample count measures."}],"resultDescription":"Returns two analytical reports: (1) normalised string output for the game_area and other string fields across all submitted rows, and (2) a two-measure correlation result quantifying the statistical relationship between input_latency_ms and sample_count across up to 200 rows. No external lookups or predictions are included.","failureModes":["Exceeding 200 rows causes a validation error","Missing required fields (row_id, date, input_latency_ms, sample_count) causes rejection","Malformed date strings not matching YYYY-MM-DD pattern are rejected","Null or out-of-range numeric values may produce incomplete correlation results","Empty rows array is not accepted (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you have controller sampling data with string fields needing normalisation and you want a paired correlation analysis between input latency and sample count in a single low-cost call. It is ideal for small to medium datasets (up to 200 rows) where you need both string cleaning and statistical correlation without building a local analytics pipeline. Prefer alternatives if you need more than two measures correlated, more than 200 rows processed, or live external data lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:31:43.180Z","isFirstParty":false,"canonicalSlug":"controller-sampling-string-normalisation-two-measure-correlation-0f8d97a4"}