---

name: cli-workflows description: "Guide Snips NLU CLI command construction for datasets, training, parsing, resources, versions, and metrics." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

CLI Workflows

Use this sub-skill when a user needs to construct, check, or troubleshoot snips-nlu / python -m snips_nlu commands for Snips NLU 0.20.x workflows: dataset JSON generation, engine training, one-shot or interactive parsing, language-resource downloads, manual resource linking, version checks, or CLI metrics.

Route Quickly

1. Start with the safe availability check in scripts/snips_nlu_cli_smoke.py. It only runs version commands and --help checks by default; it does not download resources, train, parse, or write model outputs.
2. Use references/cli-reference.md for command grammar, required positional arguments, shell quoting, resource-management commands, and end-to-end command templates.
3. Use references/evaluation.md when the task is cross-val-metrics or train-test-metrics, especially if the optional metrics dependency may be missing.
4. Use references/troubleshooting.md when a command fails, when downloads require network or pip options, when parse cannot load a trained engine, or when an output path already exists.

Boundaries

• Dataset YAML/JSON schema, builtin/custom entity data format, and resource naming concepts belong in ../dataset-and-resources/SKILL.md.
• Programmatic alternatives using SnipsNLUEngine, Dataset.from_yaml_files, load_resources, or NLUEngineConfig belong in ../engine-api/SKILL.md.
• This sub-skill owns CLI command construction and operational safeguards only; do not duplicate full dataset schema or API reference material here.

Default Entry Point Policy

Prefer python -m snips_nlu when the console script is missing or the active Python environment is ambiguous. Use snips-nlu when it is available and known to resolve to the intended Snips NLU installation. Both entry points expose the same subcommands for the workflows covered here.