csv-profilerlisted

# CSV Profiler Answers one question: can anything be built on this extract, and where will it break. ## Procedure 1. Count rows and columns before reading anything else, and compare against what the sender claimed. A silent truncation at export time is common and invisible later. 2. Infer a type per column from a sample, then verify it against the whole column. The column that is an integer in the first thousand rows and a string in row 40,000 is the one that breaks the load. 3. Report null rate per column. A column that is 98 percent null is not a column. 4. Check the claimed key for duplicates and nulls. If it has either, say so before anyone writes a join against it. 5. Range-check numerics and dates. Dates in the future and negative quantities are the two that survive every other check. 6. Flag free-text columns. They carry delimiters, newlines, and personal data, and each of those is a separate problem. See [the delimiter and encoding notes](references/missing.md) for the cases where a file parses cleanly and is still wrong. ## Output A table with one row per column: type, null rate, distinct count, min, max, and a note where the column failed a check. Then a short verdict: usable, usable with caveats, or send it back.