# Get Resource Usage

> Get resource usage metrics for nodes and pods. Uses Kubernetes metrics server to retrieve CPU and memory usage. Use for capacity planning or investigating resource issues. Keywords: resource usage, CPU, memory, metrics, capacity.

- Skill: `majiayu000/get-resource-usage` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add majiayu000/get-resource-usage`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/get-resource-usage/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/get-resource-usage

---


# Get Resource Usage

## Preconditions

Before applying this skill, verify:

- Metrics server is installed and running
- Cluster has metrics-server or equivalent

## Actions

### 1. Get Node Resource Usage

Retrieve CPU and memory usage for all nodes.

```yaml
mcp_tool: kubernetes-mcp-server/nodes_top
params: {}
timeout: 30s
```

### 2. Get Pod Resource Usage

Retrieve CPU and memory usage for pods.

```yaml
mcp_tool: kubernetes-mcp-server/pods_top
params:
  all_namespaces: true
timeout: 30s
```

### 3. Get Node Stats Summary

Get detailed stats from kubelet for specific node.

```yaml
mcp_tool: kubernetes-mcp-server/nodes_stats_summary
params:
  name: $node_name
timeout: 30s
```

## Success Criteria

The skill succeeds when:

- [ ] Metrics retrieved for all nodes
- [ ] CPU and memory values are valid
- [ ] No errors from metrics API

## Failure Handling

If metrics are unavailable:

1. Check if metrics-server is running
2. Verify node kubelet is responsive
3. Return partial data if some nodes fail

## Examples

**Output:**
```json
{
  "nodes": [
    {
      "name": "node1",
      "cpu_percent": 45,
      "memory_percent": 62,
      "cpu_cores": "2000m",
      "memory_bytes": "4Gi"
    }
  ],
  "top_pods": [
    {
      "name": "vllm-abc123",
      "namespace": "vllm",
      "cpu": "1500m",
      "memory": "16Gi"
    }
  ]
}
```