{"uid":"cap_fxYSuKP0UhxdhLMo7kw0d","slug":"data-freshness-string-normalisation-equal-width-histogram-2f75b3bc","name":"Data Freshness String Normalisation + Equal-Width Histogram","description":"Data freshness — String normalisation + Equal-width histogram: String normalisation + Equal-width histogram 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-06-031?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"bins":{"type":"integer","default":5,"maximum":30,"minimum":1},"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_EBTDJDVQe4TOS_kegS6xI","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":"Computes string normalisation and an equal-width histogram for data freshness records (age_minutes or record_count) across up to 200 rows.","exampleAgentPrompt":"Analyse my data freshness records and give me a string normalisation report plus an equal-width histogram with 10 bins on the age_minutes field — here are my rows: [{\"row_id\":\"r1\",\"dataset_group\":\"sales\",\"date\":\"2024-06-01\",\"age_minutes\":5,\"record_count\":120},{\"row_id\":\"r2\",\"dataset_group\":\"marketing\",\"date\":\"2024-06-02\",\"age_minutes\":45,\"record_count\":80}].","exampleUseCases":[{"title":"Pipeline freshness distribution audit","prompt":"I have 150 data freshness records from my ETL pipeline and I want to understand how age_minutes is distributed — can you run a string normalisation and equal-width histogram with 8 bins on the age_minutes field for all of them?"},{"title":"Record count bucketing by dataset group","prompt":"Take these freshness records for my dataset groups and produce a string normalisation report plus a 5-bin equal-width histogram on the record_count field so I can see how record volumes cluster."},{"title":"Staleness monitoring across daily snapshots","prompt":"I've got daily snapshot data with age_minutes and record_count for the past month — normalise the string labels and give me a 15-bin histogram on age_minutes so I can spot which date ranges have stale records."}],"resultDescription":"Returns two analytical reports for the supplied data freshness rows: (1) a string normalisation summary covering the text fields, and (2) an equal-width histogram of the selected numeric field (age_minutes or record_count) partitioned into the requested number of bins, including bin edges and counts per bin. No external lookups or predictions are made.","failureModes":["More than 200 rows supplied — request is rejected","Invalid date format (not YYYY-MM-DD) — validation error","bins value outside 1–30 range — validation error","Unknown field value (not age_minutes or record_count) — enum validation error","All values for the selected field are null — histogram cannot be computed","Malformed row object missing required fields — schema validation error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical profiling of data freshness records without any external API calls or predictions. It is ideal for data quality pipelines that need both string normalisation and histogram binning in a single cheap call ($0.01 USDC) on batches up to 200 rows. Prefer it over general-purpose analytics services when you specifically want equal-width histogram bucketing of age_minutes or record_count alongside string normalisation in a single combined report.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:15.702Z","isFirstParty":false,"canonicalSlug":"data-freshness-string-normalisation-equal-width-histogram-2f75b3bc"}