sawrus/areas/software/mlops/skills/experiment-tracking
areas/software/mlops/skills/experiment-tracking/SKILL.md
# Skill: Experiment Tracking (MLflow) ## When to load When running training experiments, comparing runs, or reproducing a historical experiment. ## MLflow Tracking Pattern ```python with mlflow.start_run(run_name="xgboost-lr-0.01-depth-6") as run: mlflow.log_params({ "model_type": "xgboost", "learning_rate": 0.01, "max_depth": 6, "data_version": dataset_version, "random_seed": 42, }) mlflow.set_tags({ "git_commit": subprocess.check_
$ install --global
skillsauthnpx skillsauth add sawrus/agent-guides areas/software/mlops/skills/experiment-trackingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
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SemgrepStatic code analysis for vulnerabilities
95%
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mcp-scan (Snyk)Model Context Protocol security validation
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Snyk (dep)Open source security scanning
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Socket.devSupply chain security analysis
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VirusTotalMulti-engine malware detection
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OWASP Dep-Check
50%
Last scanned: Apr 18, 2026, 4:31 AM53.3s1 file scanned
SKILL.md
Skill: Experiment Tracking (MLflow)
When to load
When running training experiments, comparing runs, or reproducing a historical experiment.
MLflow Tracking Pattern
with mlflow.start_run(run_name="xgboost-lr-0.01-depth-6") as run:
mlflow.log_params({
"model_type": "xgboost",
"learning_rate": 0.01,
"max_depth": 6,
"data_version": dataset_version,
"random_seed": 42,
})
mlflow.set_tags({
"git_commit": subprocess.check_output(["git", "rev-parse", "HEAD"]).decode().strip(),
})
model = train_model(X_train, y_train, hyperparams)
mlflow.log_metrics({"test_auc_roc": 0.847, "test_f1": 0.731})
signature = mlflow.models.infer_signature(X_train, model.predict(X_train))
mlflow.xgboost.log_model(model, "model", signature=signature)
mlflow.log_artifact("evaluation_scorecard.json")
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