{"uid":"cap_gxVLtq95biaXzpiDGLiku","slug":"null-coverage-analytics-string-normalisation-date-by-category-pivot-94f3a726","name":"Null Coverage Analytics — String Normalisation & Date-by-Category Pivot","description":"Null coverage — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied null coverage records (missing_values, total_values). 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-03-035?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_U_ZzR-VoPSbFTqrZ45PUD","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 null coverage records by running string normalization and a date-by-category pivot report on up to 200 rows of missing/total value data.","exampleAgentPrompt":"Can you run a null coverage analysis on my dataset — I have up to 200 rows with fields row_id, dataset_group, date, missing_values, and total_values — and give me both a string normalization report and a date-by-category pivot on the missing_values field?","exampleUseCases":[{"title":"Data quality audit by group","prompt":"I have a CSV of null coverage records for 150 rows across groups 'sales', 'marketing', and 'ops' with dates in October 2026. Can you pivot the missing_values by dataset_group and date, and also normalize the string fields so I can see which groups are worst for data completeness?"},{"title":"Monthly completeness pivot for pipeline","prompt":"Run a date-by-category pivot on my total_values field across these 80 null coverage records — the groups are 'users', 'transactions', and 'events' with dates from 2026-09-01 to 2026-09-30. I also need the string normalization report for the same batch."},{"title":"Spot-check missing values across two teams","prompt":"I've got 40 null coverage rows split between dataset groups 'team-alpha' and 'team-beta' across Q3 2026. Can you give me a string normalization report and a missing_values pivot by category and date so I can present a data quality summary to my manager?"}],"resultDescription":"Returns two analytical reports for the submitted batch (up to 200 rows): (1) a string normalization report cleaning and standardizing string fields, and (2) a date-by-category pivot showing the selected metric (missing_values or total_values) broken down by dataset_group across dates. All computation is local with no external lookups or predictions.","failureModes":["Batch exceeds 200 rows — request is rejected; split into smaller batches","Missing required fields (row_id, dataset_group, date, missing_values, total_values) — validation error returned","Date field does not match YYYY-MM-DD pattern — schema validation failure","Null or out-of-range numeric values for missing_values or total_values beyond ±1,000,000,000,000 — rejected","Empty rows array — minimum 1 item required"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local batch analytics on null coverage data — specifically when you want both a string normalization pass and a date-by-category pivot in a single low-cost call ($0.01 USDC). Ideal for data quality pipelines, completeness audits, or reporting workflows that already have structured records with missing_values and total_values. Not suitable when you need live data lookups, predictive modeling, or more than 200 rows per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:17.445Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-string-normalisation-date-by-category-pivot-94f3a726"}