{"uid":"cap_RwEWGwOVSl1OGe6jQKZWH","slug":"customer-segment-observations-record-quality-audit-string-434cf16b","name":"Customer Segment Observations — Record Quality Audit, String Normalisation & Category Frequencies","description":"Customer segment observations — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied customer segment observations records (account_count, revenue_amount). 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-company-16-195?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","account_count","revenue_amount"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"account_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"revenue_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","account_count":10,"company_group":"group-a","revenue_amount":1},{"date":"2026-10-02","row_id":"row-2","account_count":20,"company_group":"group-b","revenue_amount":2},{"date":"2026-10-03","row_id":"row-3","account_count":15,"company_group":"group-a","revenue_amount":3},{"date":"2026-10-04","row_id":"row-4","account_count":30,"company_group":"group-b","revenue_amount":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_zFGieqTa4xJ62bNYQCWQI","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 three analytical reports (record quality audit, string normalisation, and category frequencies) on up to 200 rows of customer segment observation data containing account counts and revenue amounts.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and category frequency report on my customer segment data? I have rows with fields: row_id, company_group (like 'group-a', 'group-b'), date (e.g. 2026-10-01), account_count, and revenue_amount — up to 200 rows total.","exampleUseCases":[{"title":"Pre-analysis data quality check","prompt":"Before I run my revenue analysis, can you audit these customer segment records for quality issues and normalise the company_group strings? I have about 50 rows with account_count and revenue_amount values, and I want to catch any nulls, inconsistencies, or formatting problems."},{"title":"Category distribution summary for segment report","prompt":"I need to know how frequently each company group appears across my 120 customer segment observation rows — can you run a category frequency breakdown on the company_group field for me?"},{"title":"Monthly segment data cleanup","prompt":"I've got this month's customer segment data — around 80 rows with date, company_group, account_count, and revenue_amount — can you run all three reports: record quality audit, string normalisation, and category frequencies, so I can clean it up before loading it into our dashboard?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging missing values, type mismatches, or out-of-range entries; (2) normalised string representations of text fields like company_group; and (3) category frequency counts showing how often each distinct category value appears across the supplied rows. All computed locally without external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, company_group, date, account_count, revenue_amount) causes row-level rejection","Malformed date strings not matching YYYY-MM-DD pattern trigger pattern validation errors","Numeric values outside ±1,000,000,000,000 bounds are rejected","Empty rows array returns an error","Payment failure via x402 protocol prevents execution"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline data quality and profiling reports on small-to-medium customer segment datasets (up to 200 rows) without sending data to external live services. It is ideal for pre-processing pipelines where you want record quality auditing, string normalisation, and category frequency summaries bundled into a single low-cost call ($0.01 USDC for all three reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T06:58:55.743Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-record-quality-audit-string-434cf16b"}