{"uid":"cap_I9rGO8CzkBa7Ppmu03GSf","slug":"numeric-precision-comparisons-string-normalisation-trailing-mean-series-e741bc3f","name":"Numeric Precision Comparisons — String Normalisation + Trailing Mean Series","description":"Numeric precision comparisons — String normalisation + Trailing mean series: String normalisation + Trailing mean series for supplied numeric precision comparisons records (source_value, transformed_value). 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-analytics-20-028?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["source_value","transformed_value"],"type":"string","default":"source_value"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_U_UH_eyWY2c84WQ1tG8qx","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 string normalisation and trailing mean series analytics on up to 200 rows of numeric precision comparison data (source_value vs transformed_value), returning two independent reports.","exampleAgentPrompt":"Can you run string normalisation and trailing mean series (window size 3) on my numeric comparison dataset for the source_value field? I have about 50 rows with row IDs, dates, dataset groups, and both source_value and transformed_value columns.","exampleUseCases":[{"title":"ETL pipeline quality check","prompt":"I just ran an ETL transformation on my sales figures and need to compare the source values against the transformed values. Can you run string normalisation and a trailing mean series with a window of 5 on my 120-row dataset using the source_value field, so I can spot where the transformation drifted?"},{"title":"Financial data precision audit","prompt":"I have 80 rows of financial records with original and adjusted numeric values across two groups — run string normalisation and a trailing mean series (window 4) on the transformed_value field so I can review numeric precision across dates."},{"title":"Sensor reading smoothing analysis","prompt":"I've got 150 rows of sensor readings split into two dataset groups with raw source_value and calibrated transformed_value columns. Please compute the trailing mean series with window size 7 and string normalisation on the source_value field and give me both reports."}],"resultDescription":"Two independent analytical reports: (1) a string normalisation report on the selected numeric field across supplied rows, and (2) a trailing mean series computed over the specified sliding window for the selected field, both derived entirely from the supplied data without external lookups or predictions.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows submitted — exceeds maxItems limit","Window size smaller than 2 or larger than 200 — validation error","source_value or transformed_value outside [-1e12, 1e12] — schema validation failure","date field not in YYYY-MM-DD format — pattern validation error","row_id missing or empty string — required field error","Payment not completed — endpoint returns 402 response"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and trailing mean series computed together on a batch of up to 200 numeric precision comparison records (source vs transformed values) in a single paid call, without requiring any external data lookup, live prediction, or third-party integration. It is cost-effective at $0.01 USDC for two bundled local analytical reports.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:32.109Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-string-normalisation-trailing-mean-series-e741bc3f"}