{"uid":"cap_5XYFv0AOCOVDDYiysT-BB","slug":"document-workflow-string-normalisation-d1d087d0","name":"Document Workflow String Normalisation","description":"Document workflow - String normalisation: String normalisation for Document workflow. Up to 200 supplied typed records. Local numeric and data-quality processing; no external enrichment or forecasting. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded string normalisation deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-analytics-document-workflow-normalise?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","department","date","documents","processing_minutes"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"documents":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"department":{"type":"string","default":"","maxLength":80},"processing_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","documents":10,"department":"group-a","processing_minutes":1},{"date":"2026-10-02","row_id":"row-2","documents":20,"department":"group-b","processing_minutes":2},{"date":"2026-10-03","row_id":"row-3","documents":15,"department":"group-a","processing_minutes":3},{"date":"2026-10-04","row_id":"row-4","documents":30,"department":"group-b","processing_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_491WRh4LIPWQ-S9Xf2f7R","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 document workflow records (department names, row IDs, dates) across up to 200 typed rows with local data-quality processing.","exampleAgentPrompt":"Can you normalise the string fields in my document workflow records? I have 4 rows: row-1 for group-a on 2026-10-01 with 10 documents and 1 processing minute, row-2 for group-b on 2026-10-02 with 20 documents and 2 minutes, row-3 for group-a on 2026-10-03 with 15 documents and 3 minutes, and row-4 for group-b on 2026-10-04 with 30 documents and 4 minutes.","exampleUseCases":[{"title":"Standardising department labels before reporting","prompt":"I've pulled document workflow records from three regional offices and the department names are all over the place — some say 'HR', some say 'h.r.', some say 'Human Resources'. Can you normalise the string fields across these 50 rows so the department column is consistent before I run my monthly report?"},{"title":"Cleaning row IDs for ETL pipeline","prompt":"Before I load my document processing data into the warehouse, I need the row IDs and department strings normalised — I have 120 rows with dates from 2026-09-01 to 2026-09-30, documents counts, and processing minutes. Can you clean those string fields up?"},{"title":"Data quality check on incoming workflow batch","prompt":"We just received a batch of 80 document workflow records from a vendor and the string fields look messy — inconsistent casing on department names and malformed row identifiers. Can you run string normalisation on these rows so I can validate the data before we accept it?"}],"resultDescription":"Returns up to 200 processed records with normalised string fields (department names, row IDs, date strings) after local data-quality transformations. No external enrichment is applied; output is a bounded, cleaned version of the submitted rows.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, department, date, documents, processing_minutes) causes record rejection","Malformed date strings not matching YYYY-MM-DD pattern fail validation","Numeric fields outside the allowed range trigger boundary errors","Empty rows array rejected due to minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local string normalisation on document workflow records (up to 200 rows) without external API dependencies. It is best suited for pre-processing department names, row identifiers, and dates before loading into analytics pipelines or reporting tools. Prefer alternatives if you need ML-based entity resolution, semantic deduplication, or enrichment from external data sources.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T06:50:31.733Z","isFirstParty":false,"canonicalSlug":"document-workflow-string-normalisation-d1d087d0"}