| name | batch-to-toon |
| description | Convert a directory of JSON / CSV / YAML files into TOON in one pass. Per-file round-trip checks, aggregate token-saving report, and a manifest so partial runs are resumable. Use when the user has a folder of structured data destined for LLM prompts. |
Batch → TOON
Convert many files at once.
Inputs
- Source directory — absolute path. Non-recursive by default; pass
--recursive to walk subdirs.
- File types — default:
.json, .csv, .yaml, .yml. Limit with --ext.
- Output directory — default:
<source>/toon/. Mirrors the input layout.
- Backend — default: read from plugin
config.json (set by setup-tooling); fall back to npx -y @toon-format/toon.
Workflow
-
Glob the source dir for matching files. Skip anything already present in the output dir with the same mtime (idempotent reruns).
-
Create or load a manifest at:
${CLAUDE_USER_DATA:-${XDG_DATA_HOME:-$HOME/.local/share}/claude-plugins}/get-toony/state/<batch-id>.json
Each row: input path, type, status (pending / done / failed / skipped-no-savings), original tokens, TOON tokens, saving %, error (if any).
-
For each file:
- JSON → use
json-to-toon skill logic (validate → encode → round-trip).
- CSV → use
csv-to-toon skill logic (CSV → JSON → encode).
- YAML → parse to JSON first (
yq -o=json or python3 -c "import yaml,json; print(json.dumps(yaml.safe_load(open(p))))"), then encode.
-
Round-trip every file. If any fails, mark failed in the manifest and continue — don't abort the batch.
-
Token saving below 5%? Mark skipped-no-savings and leave the original alone. TOON-ifying tiny savings is just churn.
Final report
At the end, print:
- Total files processed / succeeded / failed / skipped.
- Aggregate tokens: total before, total after, absolute saving, % saving.
- Worst-case file (smallest or negative saving) and best-case file.
- Manifest path for inspection.
Resumability
Re-running on the same source dir re-uses the existing manifest. done rows are skipped; pending and failed are retried. --reset forces a clean run.
Failure modes
- YAML files with anchors / merge keys may not round-trip cleanly through JSON. Flag rather than silently lose data.
- CSV files with ragged rows (different column counts per row) won't form a uniform TOON table. Either pad or fail — surface the choice to the user once, then apply consistently across the batch.
