mlflow-python

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MLflow experiment tracking via Python API. TRIGGERS - MLflow metrics, log backtest, experiment tracking, search runs.

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Sourceterrylica/cc-skills

Added on2026-02-07

NPX Install

npx skill4agent add terrylica/cc-skills mlflow-python

SKILL.md Content

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MLflow Python Skill

Unified read/write MLflow operations via Python API with QuantStats integration for comprehensive trading metrics.

ADR: 2025-12-12-mlflow-python-skill

Note: This skill uses Pandas (MLflow API requires it). The
mlflow-python
path is auto-skipped by the Polars preference hook.

When to Use This Skill

CAN Do:

Log backtest metrics (Sharpe, max_drawdown, total_return, etc.)
Log experiment parameters (strategy config, timeframes)
Create and manage experiments
Query runs with SQL-like filtering
Calculate 70+ trading metrics via QuantStats
Retrieve metric history (time-series data)

CANNOT Do:

Direct database access to MLflow backend
Artifact storage management (S3/GCS configuration)
MLflow server administration

Prerequisites

Authentication Setup

MLflow uses separate environment variables for credentials (NOT embedded in URI):

bash

# Option 1: mise + .env.local (recommended)
# Create .env.local in skill directory with:
MLFLOW_TRACKING_URI=http://mlflow.eonlabs.com:5000
MLFLOW_TRACKING_USERNAME=eonlabs
MLFLOW_TRACKING_PASSWORD=<password>

# Option 2: Direct environment variables
export MLFLOW_TRACKING_URI="http://mlflow.eonlabs.com:5000"
export MLFLOW_TRACKING_USERNAME="eonlabs"
export MLFLOW_TRACKING_PASSWORD="<password>"

Verify Connection

bash

/usr/bin/env bash << 'SKILL_SCRIPT_EOF'
cd ${CLAUDE_PLUGIN_ROOT}/skills/mlflow-python
uv run scripts/query_experiments.py experiments
SKILL_SCRIPT_EOF

Quick Start Workflows

A. Log Backtest Results (Primary Use Case)

bash

/usr/bin/env bash << 'SKILL_SCRIPT_EOF_2'
cd ${CLAUDE_PLUGIN_ROOT}/skills/mlflow-python
uv run scripts/log_backtest.py \
  --experiment "crypto-backtests" \
  --run-name "btc_momentum_v2" \
  --returns path/to/returns.csv \
  --params '{"strategy": "momentum", "timeframe": "1h"}'
SKILL_SCRIPT_EOF_2

B. Search Experiments

bash

uv run scripts/query_experiments.py experiments

C. Query Runs with Filter

bash

uv run scripts/query_experiments.py runs \
  --experiment "crypto-backtests" \
  --filter "metrics.sharpe_ratio > 1.5" \
  --order-by "metrics.sharpe_ratio DESC"

D. Create New Experiment

bash

uv run scripts/create_experiment.py \
  --name "crypto-backtests-2025" \
  --description "Q1 2025 cryptocurrency trading strategy backtests"

E. Get Metric History

bash

uv run scripts/get_metric_history.py \
  --run-id abc123 \
  --metrics sharpe_ratio,cumulative_return

QuantStats Metrics Available

The

log_backtest.py

script calculates 70+ metrics via QuantStats, including:

Category	Metrics
Ratios	sharpe, sortino, calmar, omega, treynor
Returns	cagr, total_return, avg_return, best, worst
Drawdown	max_drawdown, avg_drawdown, drawdown_days
Trade	win_rate, profit_factor, payoff_ratio, consecutive_wins/losses
Risk	volatility, var, cvar, ulcer_index, serenity_index
Advanced	kelly_criterion, recovery_factor, risk_of_ruin, information_ratio

See quantstats-metrics.md for full list.

Bundled Scripts

Script	Purpose
`log_backtest.py`	Log backtest returns with QuantStats metrics
`query_experiments.py`	Search experiments and runs (replaces CLI)
`create_experiment.py`	Create new experiment with metadata
`get_metric_history.py`	Retrieve metric time-series data

Configuration

The skill uses mise

[env]

pattern for configuration. See

.mise.toml

for defaults.

Create

.env.local

(gitignored) for credentials:

bash

MLFLOW_TRACKING_URI=http://mlflow.eonlabs.com:5000
MLFLOW_TRACKING_USERNAME=eonlabs
MLFLOW_TRACKING_PASSWORD=<password>

Reference Documentation

Authentication Patterns - Idiomatic MLflow auth
QuantStats Metrics - Full list of 70+ metrics
Query Patterns - DataFrame operations
Migration from CLI - CLI to Python API mapping

Migration from mlflow-query

This skill replaces the CLI-based

mlflow-query

skill. Key differences:

Feature	mlflow-query (old)	mlflow-python (new)
Log metrics	Not supported	`mlflow.log_metrics()`
Log params	Not supported	`mlflow.log_params()`
Query runs	CLI text parsing	DataFrame output
Metric history	Workaround only	Native support
Auth pattern	Embedded in URI	Separate env vars

See migration-from-cli.md for detailed mapping.

Troubleshooting

Issue	Cause	Solution
Connection refused	MLflow server not running	Verify MLFLOW_TRACKING_URI and server status
Authentication failed	Wrong credentials	Check MLFLOW_TRACKING_USERNAME and PASSWORD in .env
Experiment not found	Experiment name typo	Run `query_experiments.py experiments` to list all
QuantStats import error	Missing dependency	`uv add quantstats` in skill directory
Pandas import warning	Expected for this skill	Ignore - MLflow requires Pandas (hook-excluded)
Run creation fails	Experiment doesn't exist	Use `create_experiment.py` to create first
Metric history empty	Wrong run_id or metric name	Verify run_id with `query_experiments.py runs`
Returns CSV parse error	Wrong date format or columns	Check CSV has date index and returns column

mlflow-python

NPX Install

Tags

SKILL.md Content

MLflow Python Skill

When to Use This Skill

Prerequisites

Authentication Setup

Verify Connection

Quick Start Workflows

A. Log Backtest Results (Primary Use Case)

B. Search Experiments

C. Query Runs with Filter

D. Create New Experiment

E. Get Metric History

QuantStats Metrics Available

Bundled Scripts

Configuration

Reference Documentation

Migration from mlflow-query

Troubleshooting