---

name: cli-and-automation description: "Guides fg-data-profiling data_profiling and pandas_profiling CLI usage, input files, output naming, and automation patterns." disable-model-invocation: true metadata: disco-role: operating license: MIT

CLI and Automation

Use this sub-skill when the user wants shell commands, batch jobs, pipeline steps, or IDE tasks that invoke fg-data-profiling without writing a full Python script.

Read first

• Read references/cli-reference.md for the verified command names, positional arguments, flags, and input extension behavior.
• Read references/automation-recipes.md for Airflow/Python-task, shell, and IDE-style automation patterns.
• Read references/troubleshooting.md for missing executables, parser errors, file-reader limitations, and browser opening behavior.
• Run scripts/cli_smoke.py when you need a safe no-network CLI validation against a tiny generated CSV.

Primary command pattern

data_profiling --silent --minimal data.csv report.html

The legacy command name also exists:

pandas_profiling --silent --minimal data.csv report.html

Prefer data_profiling in new automation. If output_file is omitted, the CLI uses the input file stem with a .html suffix.

Verified CLI surface

The parser supports:

• --version
• -s, --silent to generate without opening a browser
• -m, --minimal for minimal configuration
• -e, --explorative for richer unicode/file/image-oriented analysis
• --pool_size <int>
• --title <text>
• --infer_dtypes and --no-infer_dtypes
• --config_file <yaml>
• positional input_file
• optional positional output_file

Use the Python API sub-skill when the user needs custom readers, advanced preprocessing, redaction logic, or programmatic summary access.

Automation boundary

CLI automation is best when each task starts from a standard local file and writes an HTML report artifact. Use the Python API for:

• private or sensitive datasets needing custom samples/redaction decisions;
• non-standard input loading or database access;
• profile comparison or JSON post-processing;
• Spark DataFrames or notebook displays;
• dynamic configuration built in Python.

For settings-file generation and HTML asset behavior, route to ../configuration-and-output/SKILL.md. For optional Spark or notebook readiness, route to ../integrations-and-backends/SKILL.md.

Safe validation

Run the bundled smoke helper before embedding a command in a larger pipeline:

python scripts/cli_smoke.py --command data_profiling

The helper creates a temporary CSV, calls the selected CLI, and verifies a report file was created. It does not download data or require the source repository checkout.