{"uid":"cap_LpWwr2eBbMLWh4pSzfpFH","slug":"transport-ridership-data-quality-suite-audit-normalisation-220cd41c","name":"Transport Ridership Data Quality Suite — Audit, Normalisation & Deduplication","description":"Transport ridership — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication for supplied transport ridership records (passenger_count, service_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-economics-05-191?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","region","date","passenger_count","service_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"region":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"service_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"passenger_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","service_count":1,"passenger_count":10},{"date":"2026-10-02","region":"group-b","row_id":"row-2","service_count":2,"passenger_count":20},{"date":"2026-10-03","region":"group-a","row_id":"row-3","service_count":3,"passenger_count":15},{"date":"2026-10-04","region":"group-b","row_id":"row-4","service_count":4,"passenger_count":30}],"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_hsgqnoUMHuWPrTV8pFblu","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 three data-quality reports (record quality audit, string normalisation, and exact row deduplication) on up to 200 transport ridership rows containing passenger and service counts.","exampleAgentPrompt":"Can you run a full data quality check on my transit ridership records — audit for bad values, normalise the region strings, and flag any exact duplicate rows? Here are up to 200 rows with fields: row_id, region, date, passenger_count, and service_count.","exampleUseCases":[{"title":"Pre-analysis ridership data cleanup","prompt":"I've got a batch of daily transit ridership records from multiple regions and I need to clean them up before importing into our dashboard — can you audit the record quality, normalise the region name strings, and remove any exact duplicate rows? The data has row_id, region, date, passenger_count, and service_count columns."},{"title":"Transit agency data validation pipeline","prompt":"We receive weekly ridership feeds from our regional operators and need a quality gate before they hit our database — please run a record quality audit, string normalisation, and exact deduplication check on this batch of up to 200 rows with passenger_count and service_count fields."},{"title":"Detecting dirty data in merged ridership exports","prompt":"I merged ridership exports from three different bus operators and I'm worried about inconsistent region labels and duplicate entries — can you normalise the strings, flag duplicate rows, and give me a record quality audit for the combined dataset?"}],"resultDescription":"Three independent reports delivered together: (1) a record quality audit flagging malformed, null, or out-of-range values in passenger_count and service_count; (2) a string normalisation report showing standardised versions of region strings; (3) an exact row deduplication report identifying and listing duplicate rows. All reports are derived purely from the supplied data with no external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, region, date, passenger_count, service_count) causes rejection","Dates not matching YYYY-MM-DD pattern fail schema validation","Numeric values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) are rejected","Empty rows array (minItems: 1 not met) causes a schema error","Payment of 0.01 USDC not provided results in 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you need all three data-quality operations — record audit, string normalisation, and exact deduplication — applied to transport ridership data in a single cost-efficient call ($0.01 USDC for all three reports). It is ideal for batch pre-processing pipelines of up to 200 rows before loading into analytics systems, and when you need offline, deterministic quality checks with no external data dependencies or side effects.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T12:54:17.958Z","isFirstParty":false,"canonicalSlug":"transport-ridership-data-quality-suite-audit-normalisation-220cd41c"}