ml-pipeline-automation
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
86
81%
Does it follow best practices?
Impact
94%
1.28xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Evaluation results
92%
-4%Daily Model Retraining Pipeline
Airflow DAG retry and branching config
retries count
50%
0%
retry_delay
100%
100%
exponential backoff
100%
100%
max_retry_delay
100%
100%
catchup disabled
100%
100%
max_active_runs
100%
100%
email_on_failure
100%
100%
email_on_retry false
100%
100%
execution_timeout
100%
100%
XCom null check
100%
100%
BranchPythonOperator
100%
100%
No hardcoded paths
100%
100%
93%
21%Hyperparameter Tuning Experiment Tracker
MLflow experiment tracking and model registry
set_tracking_uri
100%
100%
set_experiment
100%
100%
log_params
100%
100%
log both train and test metrics
0%
70%
log_model artifact
100%
100%
log confusion matrix
100%
100%
parent run for grid search
0%
100%
nested child runs
0%
100%
register_model
100%
100%
transition stage
100%
100%
archive_existing_versions
100%
100%
load from registry
100%
50%
98%
47%Data Quality Monitor for ML Pipelines
Data quality validation and drift detection
ColumnSchema dataclass
20%
100%
DataValidator class
87%
100%
required columns check
100%
100%
nullable check
50%
100%
numeric range check
100%
100%
allowed values check
100%
100%
KS test for numerical drift
33%
100%
chi-squared for categorical drift
0%
100%
drift threshold config
37%
100%
structured alert output
70%
100%
drift history stored
0%
75%
- Repository
- secondsky/claude-skills
- Commit
90d6bd7
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6
Table of Contents
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