{"uid":"cap_QURFpG3iv2SlgLUYYoFeA","slug":"museum-footfall-string-normalisation-e5142544","name":"Museum Footfall String Normalisation","description":"Museum footfall - String normalisation: String normalisation for Museum footfall. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded string normalisation report.","url":"https://market.datapackvibe.com/x402/assistant-ar-analytics-museum-footfall-normalise?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","exhibit","date","visitors","dwell_minutes"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"exhibit":{"type":"string","default":"","maxLength":80},"visitors":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dwell_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","exhibit":"group-a","visitors":10,"dwell_minutes":1},{"date":"2026-10-02","row_id":"row-2","exhibit":"group-b","visitors":20,"dwell_minutes":2},{"date":"2026-10-03","row_id":"row-3","exhibit":"group-a","visitors":15,"dwell_minutes":3},{"date":"2026-10-04","row_id":"row-4","exhibit":"group-b","visitors":30,"dwell_minutes":4}],"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_jWjM6Oze9_83yebwjHUkE","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 in museum visitor footfall records (exhibit names, dates, row IDs) for up to 200 rows, returning a bounded local analytics report.","exampleAgentPrompt":"Can you normalise the string fields in my museum footfall records? I have up to 50 rows, each with a row_id, an exhibit name like 'Egyptian Wing', a date like 2026-10-01, visitor counts, and dwell minutes — clean up any inconsistencies in the text fields and give me a normalisation report.","exampleUseCases":[{"title":"Cleaning exhibit name inconsistencies","prompt":"I've got 80 rows of museum footfall data where the exhibit names are inconsistently typed — some say 'Impressionism', others 'impressionism' or 'IMPRESSIONISM'. Can you normalise the string fields across all those rows so the exhibit labels are consistent before I run my analysis?"},{"title":"Standardising visitor log strings before reporting","prompt":"Before I hand off our monthly museum attendance report, I need to clean up the string fields in about 150 footfall records — row IDs, exhibit names, and date strings all have formatting issues. Can you run a string normalisation pass on them and tell me what was fixed?"},{"title":"Preparing footfall data for dashboard ingestion","prompt":"Our dashboard pipeline requires clean, standardised text fields in the footfall feed. I have 30 records with exhibit names and row identifiers that need normalising — dates are in YYYY-MM-DD format but exhibit strings are all over the place. Can you normalise the strings and give me the bounded report?"}],"resultDescription":"A bounded string normalisation report covering up to 200 input rows, detailing how exhibit names, dates, and row identifiers were standardised. The report is local and descriptive — no enrichment, forecasting, or external lookups are performed.","failureModes":["Rows array is empty (minItems: 1 violated) — request rejected","More than 200 rows supplied (maxItems exceeded) — request rejected","date field does not match YYYY-MM-DD pattern — row-level validation error","row_id missing or empty (minLength: 1 violated) — row rejected","Additional properties on row objects cause validation failure"],"whenToPreferThis":"Choose this endpoint when you have raw museum footfall records with inconsistent or dirty string fields (exhibit names, row IDs, dates) and need a cheap, fast, local normalisation pass before downstream analytics or dashboard ingestion. It is purpose-built for the museum/cultural-institution footfall domain and does not require external enrichment or forecasting — ideal for data-cleaning pipelines at $0.01 per batch of up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T00:52:34.398Z","isFirstParty":false,"canonicalSlug":"museum-footfall-string-normalisation-e5142544"}