{"uid":"cap_spDyId8RNp9A-gfgIzj1n","slug":"null-coverage-analytics-string-normalisation-category-frequencies-ce5b041c","name":"Null Coverage Analytics — String Normalisation & Category Frequencies","description":"Null coverage — String normalisation + Category frequencies: String normalisation + Category frequencies 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-03-024?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}}},"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_1uvH4c-Ck1W4R456q8ch8","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/missing-value coverage records by producing string normalization and category frequency reports for up to 200 rows of supplied data.","exampleAgentPrompt":"I have 4 rows of null coverage data — row-1 in group-a on 2026-10-01 has 10 missing out of 1 total, row-2 in group-b on 2026-10-02 has 20 missing out of 2 total, row-3 in group-a on 2026-10-03 has 15 missing out of 3 total, and row-4 in group-b on 2026-10-04 has 30 missing out of 4 total — can you run string normalization and category frequency reports on these?","exampleUseCases":[{"title":"Data Quality Audit by Dataset Group","prompt":"I've got 50 rows of data completeness records across groups like 'customer-data', 'transactions', and 'events' with dates and missing/total value counts — can you give me a category frequency breakdown and normalized group labels so I can see which datasets have the worst null coverage?"},{"title":"Monthly Missing Value Pattern Review","prompt":"I have 120 rows of null coverage records spanning October and November 2026 across 5 dataset groups. Can you run string normalization and category frequency analysis on them so I can spot which groups and dates have the most consistent missing value problems?"},{"title":"Pre-Pipeline Data Completeness Check","prompt":"Before I run my ETL pipeline, I want to check my 80 source records for null coverage — each row has a group name, date, and missing vs total value counts. Can you normalize the group strings and give me category frequencies so I know what I'm working with?"}],"resultDescription":"Returns two analytical reports: (1) a string normalization report that standardizes and cleans the dataset_group and other string fields across the supplied rows, and (2) a category frequency report showing how often each dataset_group appears and the distribution of missing/total values across categories. Both reports are computed locally from the submitted data with no external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Rows missing required fields (row_id, dataset_group, date, missing_values, total_values) will be rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Numeric values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) are rejected","Empty rows array (minItems: 1 not met) returns an error"],"whenToPreferThis":"Choose this endpoint when you need both string normalization and category frequency analysis on null/missing-value records in a single low-cost call. It is ideal for data quality auditing workflows where you have up to 200 rows of structured coverage data with dataset group labels, dates, and missing/total value counts, and you want offline, local analytics without any external lookup or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:15.020Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-string-normalisation-category-frequencies-ce5b041c"}