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Pod Management

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chaterm
pod-management

Kubernetes Pod 管理与调试

Overview

Publisherchaterm
Repositoryterminal-skills
Skill namepod-management
Stars
59
Forks
12
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by chaterm on GitHub. Read the source before you install it.

Installation

Install the Pod Management AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chaterm/terminal-skills/tree/main/kubernetes/pod-management
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chaterm/terminal-skills.git /tmp/terminal-skills
mkdir -p .claude/skills
cp -r /tmp/terminal-skills/kubernetes/pod-management .claude/skills/pod-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pod Management in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pod Management on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pod Management is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pod 管理与调试

概述

Pod 生命周期管理、日志查看、exec 调试等技能。

Pod 查看

bash
# 列出 Pod
kubectl get pods
kubectl get pods -o wide                    # 详细信息
kubectl get pods -A                         # 所有命名空间
kubectl get pods -n namespace
kubectl get pods -l app=nginx               # 按标签过滤
kubectl get pods --field-selector status.phase=Running

# Pod 详情
kubectl describe pod pod-name
kubectl get pod pod-name -o yaml
kubectl get pod pod-name -o jsonpath='{.status.phase}'

日志查看

bash
# 查看日志
kubectl logs pod-name
kubectl logs pod-name -c container-name     # 指定容器
kubectl logs pod-name --all-containers      # 所有容器

# 实时跟踪
kubectl logs -f pod-name
kubectl logs -f pod-name --since=1h         # 最近1小时
kubectl logs -f pod-name --tail=100         # 最后100行

# 上一个容器的日志(崩溃后)
kubectl logs pod-name --previous

# 多 Pod 日志
kubectl logs -l app=nginx                   # 按标签
kubectl logs -l app=nginx --max-log-requests=10

容器调试

exec 进入容器

bash
# 进入容器
kubectl exec -it pod-name -- /bin/bash
kubectl exec -it pod-name -- sh
kubectl exec -it pod-name -c container-name -- /bin/bash

# 执行命令
kubectl exec pod-name -- ls -la
kubectl exec pod-name -- cat /etc/hosts
kubectl exec pod-name -- env

调试容器

bash
# 使用 debug 容器(K8s 1.18+)
kubectl debug pod-name -it --image=busybox --target=container-name

# 复制 Pod 进行调试
kubectl debug pod-name -it --copy-to=debug-pod --container=debug --image=busybox

# 节点调试
kubectl debug node/node-name -it --image=busybox

端口转发

bash
# 转发到本地
kubectl port-forward pod-name 8080:80
kubectl port-forward pod-name 8080:80 --address 0.0.0.0

# 后台运行
kubectl port-forward pod-name 8080:80 &

Pod 生命周期

创建 Pod

yaml
# pod.yaml
apiVersion: v1
kind: Pod
metadata:
  name: nginx-pod
  labels:
    app: nginx
spec:
  containers:
  - name: nginx
    image: nginx:latest
    ports:
    - containerPort: 80
    resources:
      requests:
        memory: "64Mi"
        cpu: "250m"
      limits:
        memory: "128Mi"
        cpu: "500m"
    livenessProbe:
      httpGet:
        path: /
        port: 80
      initialDelaySeconds: 10
      periodSeconds: 5
    readinessProbe:
      httpGet:
        path: /
        port: 80
      initialDelaySeconds: 5
      periodSeconds: 3
bash
kubectl apply -f pod.yaml
kubectl create -f pod.yaml

删除 Pod

bash
kubectl delete pod pod-name
kubectl delete pod pod-name --force --grace-period=0   # 强制删除
kubectl delete pods -l app=nginx                        # 按标签删除
kubectl delete pods --all -n namespace                  # 删除所有

Pod 状态

bash
# 查看 Pod 状态
kubectl get pod pod-name -o jsonpath='{.status.phase}'

# 等待 Pod 就绪
kubectl wait --for=condition=Ready pod/pod-name --timeout=60s

# 查看事件
kubectl get events --field-selector involvedObject.name=pod-name

资源管理

查看资源使用

bash
# Pod 资源使用
kubectl top pods
kubectl top pods -n namespace
kubectl top pod pod-name --containers

# 按资源排序
kubectl top pods --sort-by=cpu
kubectl top pods --sort-by=memory

资源配置

yaml
resources:
  requests:
    memory: "64Mi"
    cpu: "250m"
  limits:
    memory: "128Mi"
    cpu: "500m"

常见场景

场景 1:排查 Pod 启动失败

bash
# 1. 查看 Pod 状态
kubectl get pod pod-name -o wide

# 2. 查看事件
kubectl describe pod pod-name | grep -A 20 Events

# 3. 查看日志
kubectl logs pod-name
kubectl logs pod-name --previous    # 如果容器重启

# 4. 检查镜像
kubectl get pod pod-name -o jsonpath='{.spec.containers[*].image}'

场景 2:排查 CrashLoopBackOff

bash
# 1. 查看退出原因
kubectl describe pod pod-name | grep -A 5 "Last State"

# 2. 查看上一次日志
kubectl logs pod-name --previous

# 3. 检查资源限制
kubectl describe pod pod-name | grep -A 10 "Limits"

# 4. 检查探针配置
kubectl get pod pod-name -o yaml | grep -A 10 "livenessProbe"

场景 3:临时运行调试 Pod

bash
# 运行临时 Pod
kubectl run debug --rm -it --image=busybox -- sh
kubectl run debug --rm -it --image=nicolaka/netshoot -- bash

# 在特定节点运行
kubectl run debug --rm -it --image=busybox --overrides='{"spec":{"nodeName":"node-name"}}' -- sh

场景 4:复制文件

bash
# 从 Pod 复制到本地
kubectl cp pod-name:/path/to/file ./local-file
kubectl cp namespace/pod-name:/path/to/file ./local-file

# 从本地复制到 Pod
kubectl cp ./local-file pod-name:/path/to/file

故障排查

状态可能原因排查方法
Pending资源不足/调度问题kubectl describe pod 查看事件
ImagePullBackOff镜像拉取失败检查镜像名、仓库认证
CrashLoopBackOff应用崩溃kubectl logs --previous
OOMKilled内存超限增加 memory limits
Evicted节点资源不足检查节点资源、清理 Pod
Unknown节点失联检查节点状态

Frequently asked questions

What does the Pod Management AI skill do?

Kubernetes Pod 管理与调试

Why use Pod Management on TypingMind?

Because you install it once and use it with any model. Pod Management is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pod Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chaterm/terminal-skills/tree/main/kubernetes/pod-management. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pod Management?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pod Management?

As many as you like. As long as a model supports skills, you can use Pod Management with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pod Management AI skill free?

Yes. It is published on GitHub by chaterm under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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