{"uid":"cap_vJ0oDN-6-bGzD97HXnYq_","slug":"data-freshness-string-normalisation-date-by-category-pivot-bf074241","name":"Data Freshness — String Normalisation + Date by Category Pivot","description":"Data freshness — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied data freshness records (age_minutes, record_count). 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-06-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","age_minutes","record_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"age_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"record_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","age_minutes":10,"record_count":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","age_minutes":20,"record_count":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","age_minutes":15,"record_count":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","age_minutes":30,"record_count":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["age_minutes","record_count"],"type":"string","default":"age_minutes"}}},"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_UflWijcG2JgUPSj8TdveL","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 two local analytical reports on data freshness records: string normalisation and a date-by-category pivot for age_minutes or record_count fields, across up to 200 rows.","exampleAgentPrompt":"Can you run string normalisation and a date-by-category pivot on my data freshness records? I have rows with age_minutes and record_count across groups like 'group-a' and 'group-b' dated 2026-10-01 through 2026-10-04 — use age_minutes as the field to pivot on.","exampleUseCases":[{"title":"Pipeline freshness audit by team","prompt":"I have 150 data freshness rows from our pipeline monitoring — each has a date, a dataset_group like 'finance' or 'marketing', and an age_minutes value. Can you normalize the string fields and give me a pivot of age_minutes by date and category so I can see which groups are going stale?"},{"title":"Record count trend by dataset group","prompt":"Here are 80 freshness records with record_count and dataset_group columns. Run string normalisation on the group labels and pivot record_count by date and category so I can spot which groups are losing records over time."},{"title":"Data quality check before ingestion","prompt":"Before I ingest this batch, can you normalize the string values and produce a date-by-category pivot for age_minutes across my 60 freshness rows? Groups are things like 'raw-events' and 'processed-events' with dates in October 2026."}],"resultDescription":"Returns two independent reports: (1) a string normalisation report over the supplied row data, and (2) a date-by-category pivot table aggregating the selected numeric field (age_minutes or record_count) broken down by date and dataset_group — all computed locally with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, dataset_group, date, age_minutes, record_count) cause validation errors","Invalid date format (not YYYY-MM-DD) causes schema rejection","Field selector not one of 'age_minutes' or 'record_count' causes enum validation failure","Null numeric values may affect pivot aggregation results","Payment of 0.01 USDC not included or rejected causes 402 error"],"whenToPreferThis":"Choose this endpoint when you need two bundled local analytical operations — string normalisation and a date-by-category pivot — on data freshness records (age_minutes, record_count) in a single low-cost call. Ideal for pipeline monitoring, ETL quality checks, and freshness audits where no external data, live lookups, or predictions are needed. Prefer over general analytics tools when you want a cheap, deterministic, offline computation on up to 200 freshness rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:57:29.920Z","isFirstParty":false,"canonicalSlug":"data-freshness-string-normalisation-date-by-category-pivot-bf074241"}