mlflow-python
MLflow experiment tracking via Python API. TRIGGERS - MLflow metrics, log backtest, experiment tracking, search runs.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/terrylica/cc-skills --skill mlflow-python- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads mlflow-python automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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-pythonpath is auto-skipped by the Polars preference hook.
Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.
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):
# Option 1: .env.local + uv --env-file (recommended)
# Create .env.local (gitignored) in the skill directory with the lines below,
# then run scripts as: uv run --env-file .env.local scripts/<script>.py
MLFLOW_TRACKING_URI=http://mlflow.example.com:5000
MLFLOW_TRACKING_USERNAME=<user>
MLFLOW_TRACKING_PASSWORD=<password>
# Option 2: Direct environment variables
export MLFLOW_TRACKING_URI="http://mlflow.example.com:5000"
export MLFLOW_TRACKING_USERNAME="<user>"
export MLFLOW_TRACKING_PASSWORD="<password>"
Verify Connection
/usr/bin/env bash << 'SKILL_SCRIPT_EOF'
ROOT="$(cc-plugin-root devops-tools)"
cd "$ROOT/skills/mlflow-python"
uv run scripts/query_experiments.py experiments
SKILL_SCRIPT_EOF
Quick Start Workflows
A. Log Backtest Results (Primary Use Case)
/usr/bin/env bash << 'SKILL_SCRIPT_EOF_2'
ROOT="$(cc-plugin-root devops-tools)"
cd "$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
uv run scripts/query_experiments.py experiments
C. Query Runs with Filter
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
uv run scripts/create_experiment.py \
--name "crypto-backtests-2025" \
--description "Q1 2025 cryptocurrency trading strategy backtests"
E. Get Metric History
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
Configuration comes from MLFLOW_* environment variables. Create .env.local (gitignored) for credentials and load it per command with uv run --env-file .env.local scripts/<script>.py:
MLFLOW_TRACKING_URI=http://mlflow.example.com:5000
MLFLOW_TRACKING_USERNAME=<user>
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 |
Post-Execution Reflection
After this skill completes, check before closing:
- Did the command succeed? — If not, fix the instruction or error table that caused the failure.
- Did parameters or output change? — If the underlying tool's interface drifted, update Usage examples and Parameters table to match.
- Was a workaround needed? — If you had to improvise (different flags, extra steps), update this SKILL.md so the next invocation doesn't need the same workaround.
Only update if the issue is real and reproducible — not speculative.
FAQ
What does the mlflow-python skill do?
MLflow experiment tracking via Python API. TRIGGERS - MLflow metrics, log backtest, experiment tracking, search runs.
How do I install mlflow-python?
Run npx skills add https://github.com/terrylica/cc-skills --skill mlflow-python in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/mlflow-python/ (personal) or .claude/skills/mlflow-python/ (project).
Which AI agents can use mlflow-python?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is mlflow-python safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains mlflow-python?
It is published by terrylica in terrylica/cc-skills. It has 138 recorded installs.
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