Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
80
70%
Does it follow best practices?
Impact
100%
1.25xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/ml-pipeline-automation/skills/ml-pipeline-automation/SKILL.mdProduction Airflow DAG resilience config
retries value
100%
100%
retry_delay
100%
100%
exponential backoff
0%
100%
max retry delay
0%
100%
catchup disabled
100%
100%
single concurrent run
100%
100%
failure alerting
100%
100%
XCom null check
0%
100%
no hardcoded paths
100%
100%
execution timeout
100%
100%
depends_on_past false
100%
100%
conditional deployment
50%
100%
MLflow experiment tracking and model registry
tracking URI set
100%
100%
experiment named
100%
100%
run context manager
100%
100%
run name set
0%
100%
all hyperparams logged
100%
100%
train metric logged
0%
100%
test metric logged
100%
100%
model artifact logged
100%
100%
model registered
100%
100%
multiple runs executed
100%
100%
dataset params logged
0%
100%
Data quality validation and drift detection
ColumnSchema dataclass
100%
100%
DataValidator class
87%
100%
schema validation checks
100%
100%
KS test used
100%
100%
KS p-value threshold
100%
100%
DriftMonitor class
100%
100%
alert callback invoked
100%
100%
drift threshold configurable
71%
100%
Prometheus Counter
100%
100%
Prometheus Histogram
100%
100%
Prometheus Gauge metrics
100%
100%
/metrics endpoint
83%
100%
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