{"uid":"cap_dzT4ab4571ZhlXtDnJsqL","slug":"data-freshness-string-normalisation-numeric-row-ordering-8785ca5a","name":"Data Freshness — String Normalisation + Numeric Row Ordering","description":"Data freshness — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering 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-06-025?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_8l98lFhNwRF9boNAGClZ7","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":"Normalises string fields and orders rows numerically for supplied data freshness records (age_minutes, record_count) across up to 200 rows, returning 2 independent analytical reports.","exampleAgentPrompt":"Can you normalise the string fields and sort the rows numerically by age_minutes for these data freshness records? I have 4 rows: row-1 for group-a on 2026-10-01 with age_minutes 10 and record_count 1, row-2 for group-b on 2026-10-02 with age_minutes 20 and record_count 2, row-3 for group-a on 2026-10-03 with age_minutes 15 and record_count 3, and row-4 for group-b on 2026-10-04 with age_minutes 30 and record_count 4.","exampleUseCases":[{"title":"Data pipeline freshness audit","prompt":"I have 50 rows of data freshness logs with dataset_group names and age_minutes values — can you normalise the string labels and sort the rows numerically by age_minutes so I can see which datasets are stalest?"},{"title":"Record count ordering for ETL monitoring","prompt":"Sort these 30 data ingestion freshness records numerically by record_count and normalise the dataset_group strings — I need two clean reports to review ETL batch completeness."},{"title":"Multi-group freshness comparison","prompt":"I've got freshness records across group-a and group-b for the past week, up to 200 rows — normalise the string fields and give me numeric row ordering by age_minutes so I can compare freshness across both groups."}],"resultDescription":"Two independent analytical reports covering: (1) normalised string values for the dataset_group and related string fields, and (2) rows ordered numerically by the chosen field (age_minutes or record_count). Results are based entirely on the supplied rows with no external lookups or predictions.","failureModes":["More than 200 rows supplied — exceeds maxItems limit, request rejected","Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — validation error","Invalid date format not matching YYYY-MM-DD pattern — schema validation failure","Invalid field enum value not matching age_minutes or record_count — request rejected","Empty rows array (minItems 1 not met) — validation error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of data freshness records and need both string normalisation of categorical/group fields and numeric ordering of quantitative fields (age_minutes or record_count) in a single call returning two reports. It is best when no live data lookup, external enrichment, or prediction is needed — only local analytical processing of supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T07:25:38.437Z","isFirstParty":false,"canonicalSlug":"data-freshness-string-normalisation-numeric-row-ordering-8785ca5a"}