Log MLflow metrics, parameters, and artifacts using the Python API.
npx skills add https://github.com/terrylica/cc-skills --skill mlflow-python
Please help me install this Agent Skill. Skill: mlflow-python Source: https://github.com/terrylica/cc-skills/tree/main/plugins/devops-tools/skills/mlflow-python Command: npx skills add https://github.com/terrylica/cc-skills --skill mlflow-python
Centralizes MLflow experiment logging, querying, and metric history analysis with QuantStats integration.
Log a backtest result to MLflow with a sample returns CSV
High-intent search queries and answers about installing and using this skill.
Log backtest metrics to MLflow using the Python API by initializing an MLflow experiment, recording parameters and 70+ QuantStats metrics from your backtest results, then logging them as artifacts and metric histories. The Skill handles experiment creation, run tracking, and metric persistence via MLflow server credentials.
Yes. Query MLflow experiments and runs with the Python client to retrieve metric histories and run metadata as dataframes. This enables analysis, comparison across backtests, and metric-history retrieval for reporting without manual export.
Over 70 QuantStats metrics are supported for backtest evaluation, covering returns analysis, risk measures, and strategy performance. These metrics integrate directly with MLflow logging to standardize backtest result tracking across experiments.
Yes. MLflow server access via environment credentials is required. The Skill uses the MLflow Python client to connect, log metrics, and query experiments, so your MLflow instance must be accessible and properly configured.
QuantStats provides comprehensive performance metrics—drawdown, Sharpe ratio, win rate, and more—that automatically integrate with MLflow logging. This standardizes strategy comparison and tracks metric evolution across multiple backtest runs and experiments.
Yes. Retrieve complete metric histories from MLflow runs to analyze performance trends, compare metrics across backtest iterations, and generate reports. History queries return time-series data for downstream analysis and visualization.
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