{"uid":"cap_MKl4jDcASH7qKTpVQ5fJh","slug":"null-coverage-analytics-record-quality-audit-string-normalisation-iqr-f8f76d17","name":"Null Coverage Analytics — Record Quality Audit, String Normalisation & IQR Outlier Flags","description":"Null coverage — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied null coverage records (missing_values, total_values). 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-analytics-03-203?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","missing_values","total_values"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"missing_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_values":1,"dataset_group":"group-a","missing_values":10},{"date":"2026-10-02","row_id":"row-2","total_values":2,"dataset_group":"group-b","missing_values":20},{"date":"2026-10-03","row_id":"row-3","total_values":3,"dataset_group":"group-a","missing_values":15},{"date":"2026-10-04","row_id":"row-4","total_values":4,"dataset_group":"group-b","missing_values":30}],"maxItems":200,"minItems":1},"field":{"enum":["missing_values","total_values"],"type":"string","default":"missing_values"}}},"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_VYVJGmk92JeBKisLmpoHs","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":"Audits null/missing-value coverage records by performing record quality checks, string normalisation, and IQR-based outlier flagging across up to 200 rows in three independent reports.","exampleAgentPrompt":"Can you run the null coverage quality audit on my dataset records — I've got rows with missing_values and total_values for groups 'group-a' and 'group-b' across dates in October 2026, and I want the record quality audit, string normalisation, and IQR outlier flags all in one go for the missing_values field?","exampleUseCases":[{"title":"Data pipeline null rate anomaly detection","prompt":"I have a batch of up to 150 rows from my ETL pipeline, each with a row ID, dataset group name, date, and counts for missing and total values — can you flag any outliers in the missing_values field using IQR and also give me a quality audit and string normalisation report?"},{"title":"Monthly data completeness review","prompt":"Run a quality audit on my November completeness records — I have about 80 rows with dataset_group labels, dates in YYYY-MM-DD format, and missing_values vs total_values counts. I want all three reports: quality audit, normalised strings, and IQR outlier flags on the total_values field."},{"title":"Cross-group null coverage comparison","prompt":"I'm comparing null coverage across three dataset groups over Q3 — here are 200 rows with row IDs, group names, dates, and missing vs total value counts. Can you give me the record quality audit, string normalisation, and IQR-based outlier detection on the missing_values column?"}],"resultDescription":"Returns three independent analytical reports for the submitted rows: (1) a record quality audit highlighting completeness and integrity issues, (2) a string normalisation report standardising text fields like dataset_group, and (3) IQR-based outlier flags identifying anomalous values in the selected numeric field (missing_values or total_values). Covers up to 200 input rows per call.","failureModes":["More than 200 rows submitted — exceeds maxItems limit, request rejected","missing required fields (row_id, date, missing_values, total_values) — row validation fails","date not in YYYY-MM-DD format — pattern validation error","numeric values outside ±1,000,000,000,000 range — schema rejection","empty rows array — minItems:1 violated","invalid field enum value other than 'missing_values' or 'total_values' — schema error"],"whenToPreferThis":"Choose this endpoint when you need simultaneous record quality auditing, string normalisation, and IQR outlier detection on null/missing-value coverage data in a single low-cost call. It is purpose-built for datasets with missing_values and total_values columns grouped by dataset_group and date. Prefer it over generic data quality tools when your data is already structured in this schema and you want three bundled analytical perspectives for $0.01 USDC without any external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:22.240Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-record-quality-audit-string-normalisation-iqr-f8f76d17"}