CSV to YAML Converter

This converter takes a comma-separated table -- the kind exported from a spreadsheet, a database query, or an analytics dashboard -- and turns each row into a YAML list item, with the header row supplying the keys. That shape fits a handful of genuinely common jobs rather than everything. Developers reach for it when a small lookup table, a set of feature flags, or seed data lives in a spreadsheet but the application reads YAML, so the CSV becomes a config fragment or fixtures file that drops into a repository.

It also suits lightweight data prep and handoff. If a colleague maintains records in Sheets or Excel and a teammate prefers to read or diff them as YAML, the row-per-entry layout is easier to scan and review line by line than a dense table. Students, analysts, and admins exporting a roster, an inventory list, or a tidy set of key/value attributes can use it to produce a readable structured snapshot without writing a script.

It is a poor fit when the source is not really tabular, such as documents or deeply nested records; export those from the source system instead.

The tool runs entirely in your browser. Paste CSV text or drop a file in, and conversion happens locally -- nothing is uploaded, and nothing persists once the tab is closed. The first line is read as the header and supplies the YAML keys; every following line becomes one item in a list, rendered as a `- key: value` block. You then copy the result or download it as a `.yaml` file.

Parsing is quote-aware: values wrapped in double quotes may contain commas and line breaks, a doubled quote ("") inside a quoted field is read as a literal quote, and a quote in the middle of an unquoted value is kept as an ordinary character. Carriage-return, line-feed, and Windows-style line endings are all handled. If a header cell is blank, the column is named `column_1`, `column_2`, and so on by position, so a row keeps its shape even with an unlabelled column.

Be clear about what it does not do. The delimiter is the comma -- it is not a general TSV or semicolon parser, and there are no options for custom delimiters or output styling. Cells are typed only where there is no doubt: a plain number such as 42 or 9.99 becomes a YAML number and true or false becomes a boolean; everything else is text. Repeated header names get a number (name, name_2) so no column overwrites another.

The typing rule is deliberately narrow. A value becomes a number only if it reads back as exactly the same text, so `01234`, `9.90`, `+5` and `1e3` stay text, and only `true` and `false` become booleans (`yes` and `no` stay text). Empty cells are written as empty strings (""), not nulls. If your config needs a different type for a column, adjust it after conversion.

Text that carries meaning in YAML is quoted automatically: a value containing a colon-space or ' #', a leading dash, @ or #, a leading or trailing space, or a line break is written in double quotes, so free-text fields load back exactly as they were in the CSV.

Structure and scale matter too. The output is always a flat list of single-level records -- dotted headers like `address.city` stay literal keys, they do not nest. Everything is processed in memory in the page, so very large exports can be slow or strain the browser tab; and the converter assumes UTF-8 text, so a file saved in another encoding (or with a byte-order mark) may surface odd characters.

Start at the top: confirm the header row produced the keys you expected and that no column collapsed into a `column_2`-style placeholder. Then count items -- the number of `- ` entries should match your data rows -- and open a row that contains commas, quotes, or longer free text to verify the quote-aware parser split the fields the way you intended. Finally, scan for any value that should be a number, boolean, or null and decide where those need adjusting.

The reliable last step is to run the YAML through the actual parser or application that will consume it. A quick load in your target tool will catch indentation slips, an unquoted special character, or a type mismatch far faster than reading the text by eye, and tells you the file is usable rather than merely plausible.

For records tied to accounts, payments or compliance, check the converted values against the source by hand.