From jeremylongshore-claude-code-plugins-plus-skills
Guides feature store connector operations for ML deployment including model serving, MLOps pipelines, monitoring, and production optimization.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:feature-store-connectorThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for feature store connector tasks within the ML Deployment domain.
This skill provides automated assistance for feature store connector tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with feature store connector" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Deployment skill category. Tags: mlops, serving, inference, monitoring, production
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skillsBuilds a feature store using Feast for centralized feature management, offline/online store configuration, and point-in-time correct joins for ML pipelines.
Builds ML pipelines, manages experiment tracking and model registries using Kubeflow, MLflow, and cloud-specific MLOps stacks. Useful when automating ML infrastructure or productionizing models.
Builds production ML systems with PyTorch 2.x, TensorFlow, and modern frameworks for model serving, feature engineering, A/B testing, monitoring, and infrastructure.