{"uid":"cap_3tKlByYwlIiHySwKdjRZS","slug":"null-coverage-analytics-record-quality-audit-string-normalisation-afbffc1f","name":"Null Coverage Analytics: Record Quality Audit + String Normalisation + Date-by-Category Pivot","description":"Null coverage — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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-03-206?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_pz0Ies0u2ymuCl82C9kog","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 on supplied null-coverage data (missing_values, total_values per row): a record quality audit, string normalisation, and a date-by-category pivot — up to 200 rows, all offline.","exampleAgentPrompt":"I have up to 200 null-coverage rows with row_id, dataset_group, date, missing_values, and total_values — can you run the three-report bundle (record quality audit, string normalisation, and date-by-category pivot) on them, focusing on the missing_values field?","exampleUseCases":[{"title":"Dataset completeness audit by group","prompt":"I've got a table of dataset records with missing_values and total_values for each row, grouped into 'sales' and 'inventory' categories across dates in October 2026 — run the record quality audit report on missing_values so I can see which groups have the worst completeness."},{"title":"Normalise group labels before pipeline ingestion","prompt":"Before I push my null-coverage data into the pipeline, can you run the string normalisation report on my dataset_group field? I have about 150 rows covering dates from 2026-09-01 to 2026-09-30 with missing_values and total_values filled in."},{"title":"Pivot null counts by date and category","prompt":"I need to understand how missing values trend over time across my dataset groups — take my 80 rows (with row_id, dataset_group, date, missing_values, total_values) and produce the date-by-category pivot report so I can spot which dates and groups are worst."}],"resultDescription":"Three structured analytical reports returned in a single response: (1) a record quality audit summarising null coverage per row, (2) a string normalisation report cleaning up dataset_group labels, and (3) a date-by-category pivot aggregating the chosen field (missing_values or total_values) by date and group — all computed locally with no external lookups.","failureModes":["Fewer than 1 row or more than 200 rows supplied — request rejected","Row missing required fields (row_id, dataset_group, date, missing_values, total_values) — validation error","date field not in YYYY-MM-DD format — pattern validation failure","field enum value not 'missing_values' or 'total_values' — enum rejection","numeric values outside ±1,000,000,000,000 bounds — schema error","Payment of 0.01 USDC not completed — 402 response returned"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of null-coverage data (missing_values + total_values per row with group and date labels) and need all three analytical passes — quality audit, string normalisation, and date-category pivot — in a single low-cost call. Prefer it over building custom scripts when you want offline, reproducible local analytics with no external data dependencies and a fixed $0.01 price for the full bundle.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:04.831Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-record-quality-audit-string-normalisation-afbffc1f"}