A comprehensive automation skill for managing Horizontal Pod Autoscaling, Vertical Pod Autoscaling, and KEDA event-driven triggers within Kubernetes clusters.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install k8-autoscaling
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install k8-autoscaling using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Kubernetes Autoscaling?
This skill provides a robust framework for managing Kubernetes autoscaling through a combination of native and extended tools. By leveraging HPA for horizontal growth, VPA for resource right-sizing, and KEDA for event-driven reactivity, developers can ensure their applications remain responsive while maintaining optimal resource efficiency. It integrates seamlessly with Openclaw Skills to provide a unified interface for scaling operations.
The skill enables precise control over workload lifecycle management, allowing for everything from simple CPU-based scaling to complex architectures that scale to zero based on external message queues or Prometheus metrics. It is designed for engineers who need to manage production-grade scaling policies across multiple environments and clusters with minimal manual overhead.
Kubernetes Autoscaling Use Cases
- Scaling web applications horizontally based on real-time CPU and Memory utilization.
- Automatically right-sizing container resource requests and limits using VPA recommendations to prevent OOM kills or resource waste.
- Scaling worker deployments to zero when message queues like AWS SQS are empty to reduce infrastructure costs.
- Implementing predictive scaling for known traffic patterns using Cron-based triggers.
- Managing complex multi-cluster autoscaling configurations across staging and production environments.
How Kubernetes Autoscaling Works
- The skill identifies the target Kubernetes environment and checks for the presence of the metrics-server and KEDA operator.
- Users define scaling intent using YAML manifests for HorizontalPodAutoscalers, VerticalPodAutoscalers, or KEDA ScaledObjects.
- The skill applies the configuration to the cluster and initiates monitoring of the scaling metrics.
- For event-driven workloads, KEDA triggers (like SQS, Prometheus, or Cron) are validated and authenticated via TriggerAuthentication resources.
- The skill provides real-time feedback on scaling events, resource recommendations, and potential bottlenecks through specialized diagnostic tools.
Kubernetes Autoscaling Setup
To begin using this skill within the Openclaw Skills ecosystem, ensure you have a running Kubernetes cluster and the necessary MCP tools installed.
# Apply a horizontal autoscaling manifest
apply_manifest(hpa_yaml, namespace)
# Verify KEDA installation and status
keda_detect_tool()
# List all active ScaledObjects in a specific namespace
keda_scaledobjects_list_tool(namespace)
Kubernetes Autoscaling Data Schema & Taxonomy
The skill organizes autoscaling data and metadata according to standard Kubernetes resource schemas and KEDA Custom Resource Definitions (CRDs):
| Resource Type | Managed By | Primary Function |
|---|---|---|
| HorizontalPodAutoscaler | HPA Controller | Adjusts replica counts based on CPU/RAM usage. |
| VerticalPodAutoscaler | VPA Controller | Adjusts container resource requests/limits. |
| ScaledObject | KEDA Operator | Maps external event triggers to specific Deployments. |
| ScaledJob | KEDA Operator | Scales Kubernetes Jobs based on event volume. |
| TriggerAuthentication | KEDA Operator | Manages credentials for external event sources. |
| Metrics | Metrics-Server | Provides the raw utilization data for HPA/VPA. |
Kubernetes Autoscaling Advanced Features
- Scale-to-Zero support for serverless-style cost optimization on idle workloads.
- Multi-metric scaling combining standard resource metrics with custom business metrics.
- Predictive scaling via Cron triggers to proactively handle anticipated load spikes.
- Multi-cluster context switching for managing autoscaling policies across global environments.
- Automated VPA 'Auto' mode for hands-off resource optimization and stabilization.
SKILL.md
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