tabular-ml-lab

Tabular ML Lab

Use this skill when Codex, Claude Code, or another LLM coding agent should act as a workflow agent for local, reproducible CSV binary classification baseline experiments.

Scope

• CSV input only.
• binary_classification only.
• Logistic Regression and RandomForestClassifier baselines.
• Seeded holdout split.
• No Web UI, Jupyter, AutoML backend, deployment, monitoring, or production-readiness guarantee.

Before Running

Work from the ml-agent-skills repository root.

Read the task file and confirm:

• data.format is csv.
• task.type is binary_classification.
• data.path points to an existing CSV.
• data.target exists in that CSV.
• run.random_seed is present or defaults to 42.
• Output will be written to the requested output directory.

Use assets/task.template.yaml when creating a new task file.

Commands

Preferred full workflow:

uv run atm run path/to/task.yaml --output path/to/output_dir

Use staged scripts only for debugging phase-by-phase behavior:

uv run python skills/tabular-ml-lab/scripts/inspect_dataset.py --task path/to/task.yaml --output path/to/output_dir
uv run python skills/tabular-ml-lab/scripts/train_baseline.py --task path/to/task.yaml --output path/to/output_dir
uv run python skills/tabular-ml-lab/scripts/evaluate_model.py --task path/to/task.yaml --output path/to/output_dir
uv run python skills/tabular-ml-lab/scripts/render_report.py --task path/to/task.yaml --output path/to/output_dir

If a step fails, stop and report the failing command, error, and likely fix. Do not skip ahead.

References

Load only the reference needed for the task:

• references/workflow.md for end-to-end run order and validation.
• references/artifact-contract.md for required files and metadata expectations.
• references/leakage-checklist.md for leakage policy and review rules.
• references/metrics.md for metrics and threshold report interpretation.
• references/reporting.md for user-facing summary expectations.