{"uid":"cap_6xNG1FZGwtVFiebDWPdGF","slug":"data-freshness-string-normalisation-base-100-series-1fe6e183","name":"Data Freshness — String Normalisation + Base-100 Series","description":"Data freshness — String normalisation + Base-100 series: String normalisation + Base-100 series 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-06-030?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"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_AUwRHdxWRGkVE8gYc8bIA","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 analytical reports on data freshness records: string normalisation and a base-100 indexed series for age_minutes or record_count fields, across up to 200 rows.","exampleAgentPrompt":"Can you run a string normalisation and base-100 index series on these data freshness records — use age_minutes as the field, set the base index to row 0, and include all 4 rows across group-a and group-b?","exampleUseCases":[{"title":"Pipeline freshness monitoring dashboard","prompt":"I have 150 rows of pipeline freshness data with dates, age_minutes, and record_count by dataset group. Can you normalise the string fields and generate a base-100 series for age_minutes starting from the first row so I can track relative staleness over time?"},{"title":"Record count trend indexing","prompt":"Normalise the dataset group labels and give me a base-100 indexed series for record_count across these 80 freshness records — set the base to index 5 so I can see how counts grew relative to that reference point."},{"title":"Multi-group data freshness audit","prompt":"I've got freshness records for three dataset groups across 200 dates. Run string normalisation on the group names and compute a base-100 series on age_minutes starting from row 0 so I can compare each group's freshness trends on a common scale."}],"resultDescription":"Two analytical reports returned together: (1) string-normalised versions of the categorical/label fields in the supplied rows, and (2) a base-100 indexed numeric series for the selected field (age_minutes or record_count), anchored to the specified base_index row. Results cover up to 200 input rows and are computed locally with no external data lookups.","failureModes":["Rows array empty or missing — returns validation error","More than 200 rows submitted — rejected with max-items error","Invalid date format (not YYYY-MM-DD) — schema validation failure","base_index out of range for supplied row count — error or undefined behaviour","Null values in age_minutes or record_count when that field is selected — may produce nulls in series output","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need two bundled offline analytical reports — string normalisation plus a base-100 index series — on data freshness records (age_minutes, record_count) for up to 200 rows, without requiring any live data lookup, external enrichment, or prediction. It is cost-effective at $0.01 USDC for both reports in a single call. Prefer alternatives if you need histograms, grouped summaries, duplicate detection, or distribution analysis (see sibling endpoints on the same platform).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:57:50.228Z","isFirstParty":false,"canonicalSlug":"data-freshness-string-normalisation-base-100-series-1fe6e183"}