{"uid":"cap_6l7ZWu7qZ5DivTC5uRUGc","slug":"issue-throughput-data-quality-bundle-audit-string-normalisation-d7dc0c8c","name":"Issue Throughput Data Quality Bundle: Audit + String Normalisation + Missing-Value Fill","description":"Issue throughput — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied issue throughput records (issues_opened, issues_closed). 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-software-13-208?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","component","date","issues_opened","issues_closed"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"component":{"type":"string","default":"","maxLength":80},"issues_closed":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"issues_opened":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","component":"group-a","issues_closed":1,"issues_opened":10},{"date":"2026-10-02","row_id":"row-2","component":"group-b","issues_closed":2,"issues_opened":20},{"date":"2026-10-03","row_id":"row-3","component":"group-a","issues_closed":3,"issues_opened":15},{"date":"2026-10-04","row_id":"row-4","component":"group-b","issues_closed":4,"issues_opened":30}],"maxItems":200,"minItems":1},"field":{"enum":["issues_opened","issues_closed"],"type":"string","default":"issues_opened"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_6e5M_mBUmC_vqCxqADOz8","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 data-quality reports on issue throughput records (issues_opened, issues_closed): a record quality audit, string normalisation, and explicit missing-value fill — all processed locally with no external lookups.","exampleAgentPrompt":"Can you audit and clean this batch of issue throughput records — check record quality, normalise the component name strings, and fill any missing issues_opened values using the median? I have about 50 rows with fields: row_id, component, date, issues_opened, and issues_closed.","exampleUseCases":[{"title":"Pre-analysis data cleaning for sprint metrics","prompt":"I have 120 rows of daily issue throughput data with some nulls in issues_closed and inconsistent component name casing — can you run a full quality audit, normalise the strings, and fill those missing values using the mean before I analyse trends?"},{"title":"Validating imported issue data from legacy system","prompt":"We just imported issue records from our old tracker and I suspect there are quality problems — can you audit up to 200 of these rows for record quality issues, normalise the component strings, and fill any missing issues_opened counts with zero?"},{"title":"Routine data quality check for engineering dashboard","prompt":"Can you run the three-report data quality bundle on this week's issue throughput rows — record audit, string normalisation, and median fill for issues_closed — so our dashboard doesn't break on bad data?"}],"resultDescription":"Three independent data-quality reports covering: (1) a record quality audit flagging malformed, inconsistent, or invalid rows; (2) normalised string values for text fields such as component names; and (3) explicit imputed values replacing nulls in the target numeric field (issues_opened or issues_closed) using the chosen fill method (zero, mean, or median). All reports are generated locally from the supplied data with no external lookups.","failureModes":["Exceeding 200-row limit returns a validation error","Missing required fields (row_id, component, date, issues_opened, issues_closed) cause row-level errors","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Unsupported fill method enum value returns schema validation error","Empty rows array rejected by minItems:1 constraint","Payment failure via x402 prevents processing"],"whenToPreferThis":"Choose this endpoint when you need a bundled, cost-effective ($0.01 USDC for three reports) data-quality pass on issue throughput records without requiring any live external data, predictions, or professional assessment. It is ideal for pre-processing software engineering metrics (issues opened/closed) before downstream analysis or dashboarding, especially when you need record auditing, string normalisation, and missing-value imputation in a single call for up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:33:05.833Z","isFirstParty":false,"canonicalSlug":"issue-throughput-data-quality-bundle-audit-string-normalisation-d7dc0c8c"}