{"uid":"cap_jk--cCtprP2WCS1T8uQ9b","slug":"csv-data-cleanup-service-6529fdaf","name":"CSV Data Cleanup Service","description":"Clean a CSV ($0.005 USDC): give an https url or inline csv (max 1 MB / 20k rows). Trims whitespace, optional case rules, dates to ISO 8601, lowercases emails, normalizes phone digits, merges or flags exact + fuzzy duplicate rows on key_columns. Returns cleaned CSV text + JSON report (rows in/out, dupes, columns changed). Deterministic, no AI. As is, no warranty. Nothing stored. Provided as is, with no warranty or guarantee of accuracy or fitness. Nothing is stored.","url":"https://well-016-datacleanup.mmelnyk2000.workers.dev/api/data-cleanup?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"csv":{"type":"string","description":"Inline CSV text instead of url (max 1 MB)."},"url":{"type":"string","description":"Public https URL of a CSV (max 1 MB)."},"fuzzy":{"type":"boolean","description":"Also match near-duplicates (default true)."},"dedupe":{"type":"string","description":"merge (default), flag, or none."},"case_rules":{"type":"object","description":"Map column -> lower | upper | title."},"date_order":{"type":"string","description":"mdy (default) or dmy for ambiguous dates."},"has_header":{"type":"boolean","description":"First row is a header (default true)."},"key_columns":{"type":"array","description":"Columns used to detect duplicates (default all)."},"date_columns":{"type":"array","description":"Columns to convert to ISO 8601 (default auto)."},"phone_format":{"type":"string","description":"digits (default) or e164."},"email_columns":{"type":"array","description":"Email columns (default auto)."},"phone_columns":{"type":"array","description":"Phone columns (default auto by header)."},"fuzzy_threshold":{"type":"number","description":"Similarity 0.5-1 (default 0.92)."}}},"responseSchema":{"type":"object","additionalProperties":true},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","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":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_vjPDyZ6VgUjRhy6XJJW5J","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Cleans and normalizes a CSV file by trimming whitespace, standardizing dates to ISO 8601, normalizing emails and phone numbers, applying case rules, and deduplicating rows with exact or fuzzy matching.","exampleAgentPrompt":"Can you clean up this CSV — trim whitespace, convert dates to ISO 8601 (dates are in month-day-year order), lowercase all emails, normalize phones to digits only, and merge exact and fuzzy duplicate rows using the 'email' and 'phone' columns as keys? Here's the URL: https://example.com/contacts.csv","exampleUseCases":[{"title":"Deduplicate customer contact list","prompt":"I have a messy customer contact CSV at https://example.com/customers.csv — can you clean it up by merging duplicate rows using the email and phone columns as keys, normalizing all phone numbers to digits only, lowercasing emails, and converting any dates to ISO 8601? Use month-day-year order for ambiguous dates."},{"title":"Normalize exported CRM data","prompt":"I exported our CRM data as a CSV — here it is inline: [csv text]. Please trim all whitespace, convert the 'signup_date' and 'last_contact' columns to ISO 8601, uppercase the 'country' column, and flag any near-duplicate rows rather than merging them, using a fuzzy threshold of 0.85."},{"title":"Standardize bulk import file before upload","prompt":"Before I import this product catalog CSV from https://example.com/products.csv into our database, can you clean it — trim whitespace, lowercase the 'sku' column, convert date columns to ISO 8601 using day-month-year order, and remove exact duplicates with no fuzzy matching?"}],"resultDescription":"Returns a JSON object containing the cleaned CSV as text and a report with row counts (rows in and rows out), number of duplicates detected/merged/flagged, and a list of columns that were changed during processing.","failureModes":["CSV exceeds 1 MB or 20,000 rows — request rejected","URL is not publicly accessible or not HTTPS — fetch fails","Invalid CSV format or encoding — parsing error returned","fuzzy_threshold outside 0.5–1.0 range — validation error","Payment of 0.005 USDC not completed — 402 response returned","Specified key_columns not found in CSV headers — error in report"],"whenToPreferThis":"Choose this endpoint when you need deterministic, rule-based CSV cleaning without AI inference — it is ideal for preprocessing data pipelines, normalizing contact lists, or standardizing exports before database imports. It handles up to 1 MB / 20k rows per call and is cheap at $0.005 USDC. Prefer it over AI-based cleaning when reproducibility and no-storage guarantees matter.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:32:34.843Z","isFirstParty":false,"canonicalSlug":"csv-data-cleanup-service-6529fdaf"}