csv-to-toon

CSV → TOON

Convert a CSV file into TOON. The reference CLI doesn't accept CSV directly, so this skill goes CSV → JSON → TOON.

Inputs

1. Source CSV — absolute path or URL.
2. Has header row? — default: yes. If no, the user must supply field names.
3. Delimiter — default ,. Detect TSV (\t) automatically by extension.
4. Type coercion — default: try to coerce numerics and booleans (true/false/null); fall back to strings. The user can disable to keep everything as strings.
5. Output path — default: same basename with .toon extension.

Workflow

1. Validate the CSV parses cleanly:

   python3 -c "import csv,sys; list(csv.DictReader(open(sys.argv[1])))" <src>
   

2. Convert to a JSON array of objects (this is the "obvious" CSV-to-JSON shape and the one TOON's tabular form is built for):

   import csv, json
   with open(src) as f:
       rows = list(csv.DictReader(f, delimiter=delim))
   # type-coerce numerics / booleans / nulls if enabled
   json.dump(rows, open(tmp_json, 'w'))
   

3. Encode to TOON:

   npx -y @toon-format/toon encode --input <tmp_json> --output <dst>
   

   For uniform CSV-derived data, TOON should pick the tabular arr[N]{f1,f2,...} form automatically — that's the whole point.

4. Round-trip check — decode TOON, re-emit as CSV with the same column order, diff against the original (after stripping trailing newlines / re-quoting differences).

5. Token delta — count tokens on <src> and <dst> and report. CSV is already compact, so the absolute saving is usually small but the structural guardrails matter for LLM accuracy. State both.

When to push back

If the user wants this purely so a downstream non-LLM consumer can read TOON instead of CSV, that's almost certainly a mistake — say so and suggest staying on CSV. TOON's value is on the LLM-input side.