{"uid":"cap_xOtI19ZNKQEmq1ooeYjHs","slug":"24k-labs-csv-profile-e5add4b8","name":"24K Labs CSV Profile","description":"Column-level statistics for a CSV: inferred type, count, null count, distinct count, min/max/mean for numeric columns, and sample values. One-call data audit.","url":"https://api.24klabs.ai/api/v1/csv-profile","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"csv":{"type":"string"}}},"responseSchema":{"type":"json","example":{"rows":3,"columns":[{"name":"price","type":"number"}]}},"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_Fak_BwgC52M-jSuWvSjsb","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":"Computes column-level statistics for a CSV file including inferred type, null count, distinct count, min/max/mean for numeric columns, and sample values.","exampleAgentPrompt":"Can you profile my CSV and tell me the data type, null count, distinct count, and min/max/mean for each column, plus a few sample values? Here's the CSV content: [paste CSV here].","exampleUseCases":[{"title":"Pre-pipeline data quality check","prompt":"Before I load this CSV into our database, can you profile each column — I want to know the inferred types, how many nulls there are, distinct counts, and the min/max for any numeric fields?"},{"title":"Exploratory analysis of uploaded dataset","prompt":"I just got this CSV from a vendor and have no idea what's in it. Can you run a column-level audit on it and show me the type, null rate, distinct values, and some sample values for each column?"},{"title":"Detecting anomalies before ML training","prompt":"I need to audit my training CSV before feeding it into a model — can you check each column for inferred type, null counts, distinct counts, and the min/max/mean of numeric columns so I can spot any obvious data issues?"}],"resultDescription":"Returns per-column statistics including inferred data type (string, integer, float, boolean, date, etc.), total count, null count, distinct count, and for numeric columns: min, max, and mean values, plus a list of sample values drawn from the column.","failureModes":["Malformed or unparseable CSV returns an error","Empty CSV or CSV with no rows returns minimal stats","Very large CSV may time out or exceed payload limits","Columns with all-null values may have limited statistics","Ambiguous types (e.g. mixed numeric/string) may result in 'string' inference"],"whenToPreferThis":"Use this endpoint when you need a fast, one-call summary of a CSV file's structure and data quality — especially before ETL pipelines, ML training, or data validation workflows. It's ideal when you want column-level profiling without standing up a full data warehouse or pandas environment. Prefer this over manual inspection or generic code execution when you need structured, machine-readable statistics quickly.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T18:47:58.406Z","isFirstParty":false}