{"uid":"cap_T07QDMtkWr3pUfvqB-jOv","slug":"numeric-precision-comparisons-string-normalisation-category-frequencies-49cb10f0","name":"Numeric Precision Comparisons — String Normalisation & Category Frequencies","description":"Numeric precision comparisons — String normalisation + Category frequencies: String normalisation + Category frequencies 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-20-024?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}}},"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_ndQ-ruS2Myymqv5BkIY6f","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":"Analyzes up to 200 rows of numeric source/transformed value pairs, producing two local reports: string normalisation analysis and category frequency counts grouped by dataset_group.","exampleAgentPrompt":"I have up to 200 rows of paired numeric readings — each with a row ID, a dataset group, a date, an original source value, and a transformed value. Can you run string normalisation and category frequency analysis on them so I can see how the numbers differ and how often each group appears?","exampleUseCases":[{"title":"ETL pipeline transformation audit","prompt":"I ran an ETL pipeline that converts raw sales figures and I want to audit the results. Here are 150 rows each with a row_id, date, dataset_group like 'region-east' or 'region-west', the original source_value, and the transformed_value after currency conversion. Can you give me a string normalisation report and tell me how frequently each region group shows up?"},{"title":"Scientific measurement precision check","prompt":"I have 80 sensor readings split across groups 'calibrated' and 'uncalibrated' — each row has a row_id, a date in 2024, a source_value from the raw sensor, and a transformed_value after my correction formula. Please run precision comparison analysis and show me the category frequency breakdown for both groups."},{"title":"Financial data transformation QA","prompt":"Before I sign off on this month's data load, I need to verify 200 rows of account balance transformations. Each row has a row_id, date, dataset_group such as 'domestic' or 'international', the pre-transform source_value, and the post-transform transformed_value. Can you produce the string normalisation report and category frequency counts for me?"}],"resultDescription":"Two analytical reports generated locally from supplied data: (1) a string normalisation report covering the numeric precision comparison pairs, and (2) category frequency counts showing how many rows belong to each dataset_group. No external lookups or live data are involved.","failureModes":["Submitting more than 200 rows returns an error or truncation","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) cause validation failure","Date fields not matching YYYY-MM-DD pattern are rejected","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array (minItems: 1 violated) returns an error"],"whenToPreferThis":"Choose this endpoint when you have a batch of paired numeric records (source vs transformed values) grouped by category and want two quick offline reports — string normalisation and frequency counts — without needing live lookups, external data enrichment, or predictions. It is ideal for data quality audits, ETL validation, and category distribution checks on datasets up to 200 rows at very low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:31.805Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-string-normalisation-category-frequencies-49cb10f0"}