容器镜像管理 logo

容器镜像管理

Organization
microwind
容器镜像管理

当管理容器镜像仓库时,分析镜像存储策略,优化镜像分发性能,解决镜像安全问题。验证镜像架构,设计CI/CD流水线,和最佳实践。

Overview

Publishermicrowind
Repositoryai-skills
Skill name容器镜像管理
Stars
93
Forks
25
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the 容器镜像管理 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/microwind/ai-skills/tree/main/cloud-native/container-registry
  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/microwind/ai-skills.git /tmp/ai-skills
mkdir -p .claude/skills
cp -r /tmp/ai-skills/cloud-native/container-registry .claude/skills/microwind-8
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 容器镜像管理 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 容器镜像管理 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 容器镜像管理 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.

容器镜像管理技能

概述

容器镜像是云原生应用的核心载体。不当的镜像管理会导致存储浪费、分发缓慢和安全风险。在设计镜像管理策略前需要仔细分析需求。

核心原则: 好的镜像管理应该提升分发效率,同时保证安全性和可追溯性。坏的镜像管理会增加存储成本,甚至引入安全漏洞。

何时使用

始终:

  • 设计容器化应用架构时
  • 优化镜像构建和分发流程时
  • 管理镜像仓库和存储时
  • 实施镜像安全策略时
  • 建立CI/CD流水线时

触发短语:

  • "优化Docker镜像"
  • "镜像仓库管理"
  • "容器镜像安全"
  • "镜像分层优化"
  • "多架构镜像"
  • "镜像分发策略"

容器镜像管理功能

镜像构建优化

  • 多阶段构建分析
  • 镜像层优化
  • 基础镜像选择
  • 构建缓存策略
  • .dockerignore优化

镜像存储管理

  • 镜像仓库架构
  • 存储空间优化
  • 镜像生命周期管理
  • 垃圾回收策略
  • 备份和恢复

镜像分发优化

  • CDN分发策略
  • 镜像预热机制
  • P2P分发方案
  • 区域镜像仓库
  • 镜像加速器

镜像安全管理

  • 镜像漏洞扫描
  • 内容信任机制
  • 签名和验证
  • 访问控制
  • 安全策略执行

常见容器镜像问题

镜像体积过大

问题:
Docker镜像体积过大,影响存储和分发

错误示例:
- 使用完整的基础镜像
- 包含不必要的文件
- 没有使用多阶段构建
- 缺少.dockerignore文件

解决方案:
1. 使用alpine等轻量基础镜像
2. 实施多阶段构建
3. 优化.dockerignore文件
4. 清理包管理器缓存
5. 合并RUN指令减少层数

镜像构建缓慢

问题:
Docker镜像构建过程耗时过长

错误示例:
- 没有利用构建缓存
- 频繁变更的层在前
- 重复安装依赖
- 网络下载速度慢

解决方案:
1. 优化Dockerfile层顺序
2. 利用构建缓存机制
3. 使用本地镜像仓库
4. 预下载基础镜像
5. 并行构建优化

镜像安全问题

问题:
容器镜像存在安全漏洞和风险

错误示例:
- 使用过时的基础镜像
- 包含已知漏洞的软件包
- 以root用户运行应用
- 敏感信息泄露到镜像中

解决方案:
1. 定期更新基础镜像
2. 实施镜像漏洞扫描
3. 使用非特权用户运行
4. 加密敏感配置信息
5. 实施镜像签名验证

代码实现示例

镜像优化分析器

python
import os
import json
import hashlib
import subprocess
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass
from pathlib import Path

@dataclass
class ImageLayer:
    """镜像层信息"""
    digest: str
    size: int
    command: str
    created: str
    files: List[str]

@dataclass
class ImageInfo:
    """镜像信息"""
    id: str
    name: str
    tag: str
    size: int
    layers: List[ImageLayer]
    created: str
    architecture: str
    os: str

@dataclass
class OptimizationIssue:
    """优化问题"""
    severity: str  # critical, high, medium, low
    type: str
    message: str
    suggestion: str
    line_number: Optional[int] = None

class ContainerImageAnalyzer:
    def __init__(self):
        self.images: List[ImageInfo] = []
        self.issues: List[OptimizationIssue] = []
        
    def analyze_image(self, image_name: str) -> Dict[str, Any]:
        """分析容器镜像"""
        try:
            # 获取镜像信息
            image_info = self.get_image_info(image_name)
            
            # 分析镜像层
            layer_analysis = self.analyze_layers(image_info)
            
            # 检查安全问题
            security_analysis = self.analyze_security(image_info)
            
            # 分析构建优化
            optimization_analysis = self.analyze_optimization(image_name)
            
            # 生成报告
            report = {
                'image_info': image_info,
                'layer_analysis': layer_analysis,
                'security_analysis': security_analysis,
                'optimization_analysis': optimization_analysis,
                'recommendations': self.generate_recommendations(),
                'score': self.calculate_image_score()
            }
            
            return report
            
        except Exception as e:
            return {'error': f'分析镜像失败: {e}'}
    
    def get_image_info(self, image_name: str) -> ImageInfo:
        """获取镜像信息"""
        try:
            # 使用docker inspect获取镜像详情
            result = subprocess.run(
                ['docker', 'inspect', image_name],
                capture_output=True,
                text=True,
                check=True
            )
            
            data = json.loads(result.stdout)[0]
            
            # 解析镜像层信息
            layers = []
            for layer_data in data.get('RootFS', {}).get('Layers', []):
                layer = ImageLayer(
                    digest=layer_data,
                    size=0,  # 需要额外获取
                    command="",
                    created="",
                    files=[]
                )
                layers.append(layer)
            
            return ImageInfo(
                id=data['Id'].split(':')[1][:12],
                name=data['RepoTags'][0].split(':')[0] if data['RepoTags'] else 'unnamed',
                tag=data['RepoTags'][0].split(':')[1] if data['RepoTags'] else 'latest',
                size=data.get('Size', 0),
                layers=layers,
                created=data['Created'],
                architecture=data.get('Architecture', 'unknown'),
                os=data.get('Os', 'unknown')
            )
            
        except subprocess.CalledProcessError as e:
            raise Exception(f'获取镜像信息失败: {e.stderr}')
        except json.JSONDecodeError as e:
            raise Exception(f'解析镜像信息失败: {e}')
    
    def analyze_layers(self, image_info: ImageInfo) -> Dict[str, Any]:
        """分析镜像层"""
        analysis = {
            'total_layers': len(image_info.layers),
            'total_size': image_info.size,
            'average_layer_size': 0,
            'large_layers': [],
            'duplicate_layers': [],
            'optimization_suggestions': []
        }
        
        if analysis['total_layers'] > 0:
            analysis['average_layer_size'] = analysis['total_size'] // analysis['total_layers']
        
        # 检查层大小异常
        for i, layer in enumerate(image_info.layers):
            if layer.size > analysis['average_layer_size'] * 2:
                analysis['large_layers'].append({
                    'layer_index': i,
                    'size': layer.size,
                    'command': layer.command
                })
        
        # 生成优化建议
        if analysis['total_layers'] > 20:
            analysis['optimization_suggestions'].append({
                'priority': 'high',
                'message': '镜像层数过多',
                'suggestion': '合并RUN指令减少层数'
            })
        
        if analysis['large_layers']:
            analysis['optimization_suggestions'].append({
                'priority': 'medium',
                'message': '存在过大的镜像层',
                'suggestion': '检查大层中的内容,优化构建步骤'
            })
        
        return analysis
    
    def analyze_security(self, image_info: ImageInfo) -> Dict[str, Any]:
        """分析镜像安全"""
        security_analysis = {
            'base_image_vulnerabilities': [],
            'user_privileges': 'unknown',
            'exposed_ports': [],
            'security_issues': [],
            'recommendations': []
        }
        
        # 检查基础镜像
        if 'ubuntu' in image_info.name.lower() or 'debian' in image_info.name.lower():
            security_analysis['security_issues'].append({
                'severity': 'medium',
                'type': 'base_image',
                'message': '使用完整的基础镜像可能增加安全风险',
                'suggestion': '考虑使用alpine等轻量基础镜像'
            })
        
        # 检查暴露端口
        security_analysis['recommendations'].append({
            'priority': 'low',
            'message': '定期扫描镜像漏洞',
            'suggestion': '使用trivy、clair等工具进行安全扫描'
        })
        
        return security_analysis
    
    def analyze_optimization(self, image_name: str) -> Dict[str, Any]:
        """分析构建优化"""
        optimization_analysis = {
            'dockerfile_exists': False,
            'dockerignore_exists': False,
            'multi_stage_build': False,
            'build_cache_usage': 'unknown',
            'optimization_score': 0,
            'suggestions': []
        }
        
        # 检查Dockerfile
        if os.path.exists('Dockerfile'):
            optimization_analysis['dockerfile_exists'] = True
            dockerfile_analysis = self.analyze_dockerfile('Dockerfile')
            optimization_analysis.update(dockerfile_analysis)
        
        # 检查.dockerignore
        if os.path.exists('.dockerignore'):
            optimization_analysis['dockerignore_exists'] = True
        else:
            optimization_analysis['suggestions'].append({
                'priority': 'high',
                'message': '缺少.dockerignore文件',
                'suggestion': '创建.dockerignore文件排除不必要的文件'
            })
        
        return optimization_analysis
    
    def analyze_dockerfile(self, dockerfile_path: str) -> Dict[str, Any]:
        """分析Dockerfile"""
        analysis = {
            'multi_stage_build': False,
            'from_instructions': [],
            'run_instructions': [],
            'copy_instructions': [],
            'user_instruction': False,
            'optimization_issues': []
        }
        
        try:
            with open(dockerfile_path, 'r', encoding='utf-8') as f:
                lines = f.readlines()
            
            for line_num, line in enumerate(lines, 1):
                line = line.strip()
                
                if line.startswith('FROM'):
                    analysis['from_instructions'].append({
                        'line': line_num,
                        'instruction': line
                    })
                    if 'as ' in line.lower():
                        analysis['multi_stage_build'] = True
                
                elif line.startswith('RUN'):
                    analysis['run_instructions'].append({
                        'line': line_num,
                        'instruction': line
                    })
                
                elif line.startswith('COPY') or line.startswith('ADD'):
                    analysis['copy_instructions'].append({
                        'line': line_num,
                        'instruction': line
                    })
                
                elif line.startswith('USER'):
                    analysis['user_instruction'] = True
            
            # 检查优化问题
            if len(analysis['from_instructions']) > 3:
                analysis['optimization_issues'].append({
                    'severity': 'medium',
                    'line': line_num,
                    'message': 'FROM指令过多,可能影响构建效率',
                    'suggestion': '考虑使用多阶段构建优化'
                })
            
            if not analysis['user_instruction']:
                analysis['optimization_issues'].append({
                    'severity': 'high',
                    'line': line_num,
                    'message': '没有设置USER指令,可能以root用户运行',
                    'suggestion': '添加USER指令使用非特权用户'
                })
            
            # 检查RUN指令合并
            if len(analysis['run_instructions']) > 5:
                analysis['optimization_issues'].append({
                    'severity': 'medium',
                    'line': line_num,
                    'message': 'RUN指令较多,建议合并减少层数',
                    'suggestion': '使用&&合并多个RUN指令'
                })
        
        except Exception as e:
            analysis['error'] = f'分析Dockerfile失败: {e}'
        
        return analysis
    
    def generate_recommendations(self) -> List[Dict[str, str]]:
        """生成优化建议"""
        recommendations = []
        
        # 基于分析结果生成建议
        for issue in self.issues:
            recommendations.append({
                'priority': issue.severity,
                'type': issue.type,
                'message': issue.message,
                'suggestion': issue.suggestion
            })
        
        # 通用优化建议
        recommendations.extend([
            {
                'priority': 'high',
                'type': 'security',
                'message': '定期更新基础镜像',
                'suggestion': '设置自动化流程更新基础镜像和依赖包'
            },
            {
                'priority': 'medium',
                'type': 'performance',
                'message': '优化镜像层缓存',
                'suggestion': '将变化频率低的操作放在Dockerfile前面'
            },
            {
                'priority': 'low',
                'type': 'maintenance',
                'message': '清理无用镜像',
                'suggestion': '定期清理无用的镜像和容器释放存储空间'
            }
        ])
        
        return recommendations
    
    def calculate_image_score(self) -> int:
        """计算镜像评分"""
        score = 100
        
        for issue in self.issues:
            if issue.severity == 'critical':
                score -= 30
            elif issue.severity == 'high':
                score -= 20
            elif issue.severity == 'medium':
                score -= 10
            elif issue.severity == 'low':
                score -= 5
        
        return max(0, score)

# 镜像仓库管理器
class ImageRegistryManager:
    def __init__(self, registry_url: str):
        self.registry_url = registry_url
        self.repositories: List[Dict] = []
        
    def list_repositories(self) -> List[Dict]:
        """列出所有仓库"""
        try:
            # 使用registry API列出仓库
            result = subprocess.run(
                ['curl', f'{self.registry_url}/v2/_catalog'],
                capture_output=True,
                text=True,
                check=True
            )
            
            data = json.loads(result.stdout)
            self.repositories = data.get('repositories', [])
            
            return self.repositories
            
        except Exception as e:
            raise Exception(f'获取仓库列表失败: {e}')
    
    def get_repository_tags(self, repository: str) -> List[str]:
        """获取仓库标签"""
        try:
            result = subprocess.run(
                ['curl', f'{self.registry_url}/v2/{repository}/tags/list'],
                capture_output=True,
                text=True,
                check=True
            )
            
            data = json.loads(result.stdout)
            return data.get('tags', [])
            
        except Exception as e:
            raise Exception(f'获取仓库标签失败: {e}')
    
    def cleanup_unused_images(self, days_old: int = 30) -> Dict[str, Any]:
        """清理未使用的镜像"""
        cleanup_report = {
            'deleted_images': [],
            'freed_space': 0,
            'errors': []
        }
        
        try:
            # 获取所有镜像
            result = subprocess.run(
                ['docker', 'images', '--format', 'json'],
                capture_output=True,
                text=True,
                check=True
            )
            
            # 分析镜像使用情况
            # 清理逻辑实现
            
            return cleanup_report
            
        except Exception as e:
            cleanup_report['errors'].append(str(e))
            return cleanup_report

# 使用示例
def main():
    # 分析镜像
    analyzer = ContainerImageAnalyzer()
    report = analyzer.analyze_image('nginx:latest')
    
    print("镜像分析报告:")
    print(f"镜像ID: {report['image_info'].id}")
    print(f"镜像大小: {report['image_info'].size / 1024 / 1024:.2f} MB")
    print(f"层数: {report['layer_analysis']['total_layers']}")
    print(f"评分: {report['score']}")
    
    # 仓库管理
    registry = ImageRegistryManager('https://registry.example.com')
    repositories = registry.list_repositories()
    print(f"仓库数量: {len(repositories)}")

if __name__ == '__main__':
    main()

镜像构建优化器

python
import os
import re
import json
from typing import List, Dict, Any, Optional
from pathlib import Path

class DockerfileOptimizer:
    def __init__(self, dockerfile_path: str):
        self.dockerfile_path = dockerfile_path
        self.original_content = ""
        self.optimized_content = ""
        self.optimizations = []
        
    def optimize_dockerfile(self) -> Dict[str, Any]:
        """优化Dockerfile"""
        try:
            # 读取原始内容
            with open(self.dockerfile_path, 'r', encoding='utf-8') as f:
                self.original_content = f.read()
            
            # 执行优化
            self.optimized_content = self.original_content
            self.optimize_base_image()
            self.optimize_run_instructions()
            self.optimize_copy_instructions()
            self.optimize_layer_order()
            self.add_security_best_practices()
            
            # 生成报告
            report = {
                'original_size': len(self.original_content),
                'optimized_size': len(self.optimized_content),
                'optimizations': self.optimizations,
                'estimated_reduction': self.estimate_size_reduction(),
                'optimized_dockerfile': self.optimized_content
            }
            
            return report
            
        except Exception as e:
            return {'error': f'优化失败: {e}'}
    
    def optimize_base_image(self) -> None:
        """优化基础镜像"""
        lines = self.optimized_content.split('\n')
        optimized_lines = []
        
        for line in lines:
            if line.strip().startswith('FROM'):
                # 检查是否可以使用更小的基础镜像
                if 'ubuntu' in line or 'debian' in line:
                    # 建议使用alpine
                    new_line = line.replace('ubuntu', 'alpine').replace('debian', 'alpine')
                    optimized_lines.append(f"# {line}  # 原始基础镜像")
                    optimized_lines.append(new_line)
                    self.optimizations.append({
                        'type': 'base_image',
                        'message': '使用alpine基础镜像减少体积',
                        'original': line,
                        'optimized': new_line
                    })
                else:
                    optimized_lines.append(line)
            else:
                optimized_lines.append(line)
        
        self.optimized_content = '\n'.join(optimized_lines)
    
    def optimize_run_instructions(self) -> None:
        """优化RUN指令"""
        lines = self.optimized_content.split('\n')
        optimized_lines = []
        run_commands = []
        
        for line in lines:
            if line.strip().startswith('RUN'):
                # 收集RUN指令
                run_commands.append(line.strip()[4:].strip())
            else:
                if run_commands and line.strip().startswith('RUN') == False:
                    # 合并之前的RUN指令
                    if len(run_commands) > 1:
                        merged_command = ' && '.join(run_commands)
                        optimized_lines.append(f'RUN {merged_command}')
                        self.optimizations.append({
                            'type': 'run_merge',
                            'message': f'合并{len(run_commands)}个RUN指令减少层数',
                            'original': run_commands,
                            'optimized': merged_command
                        })
                    elif run_commands:
                        optimized_lines.append(f'RUN {run_commands[0]}')
                    run_commands = []
                
                optimized_lines.append(line)
        
        # 处理最后的RUN指令
        if run_commands:
            if len(run_commands) > 1:
                merged_command = ' && '.join(run_commands)
                optimized_lines.append(f'RUN {merged_command}')
                self.optimizations.append({
                    'type': 'run_merge',
                    'message': f'合并{len(run_commands)}个RUN指令减少层数',
                    'original': run_commands,
                    'optimized': merged_command
                })
            else:
                optimized_lines.append(f'RUN {run_commands[0]}')
        
        self.optimized_content = '\n'.join(optimized_lines)
    
    def optimize_copy_instructions(self) -> None:
        """优化COPY指令"""
        lines = self.optimized_content.split('\n')
        optimized_lines = []
        
        for line in lines:
            if line.strip().startswith('ADD'):
                # 建议使用COPY替代ADD
                if 'http' not in line and 'tar' not in line.lower():
                    new_line = line.replace('ADD', 'COPY')
                    optimized_lines.append(f"# {line}  # ADD指令")
                    optimized_lines.append(new_line)
                    self.optimizations.append({
                        'type': 'add_to_copy',
                        'message': '使用COPY替代ADD提高安全性',
                        'original': line,
                        'optimized': new_line
                    })
                else:
                    optimized_lines.append(line)
            else:
                optimized_lines.append(line)
        
        self.optimized_content = '\n'.join(optimized_lines)
    
    def optimize_layer_order(self) -> None:
        """优化层顺序"""
        # 简化实现:添加注释说明
        self.optimized_content = "# 优化层顺序:将变化频率低的操作放在前面\n" + self.optimized_content
        self.optimizations.append({
            'type': 'layer_order',
            'message': '建议优化层顺序以提高缓存效率',
            'suggestion': '将依赖安装等变化少的操作放在前面'
        })
    
    def add_security_best_practices(self) -> None:
        """添加安全最佳实践"""
        security_lines = [
            "# 安全最佳实践",
            "RUN addgroup -g 1000 appgroup && adduser -D -s /bin/sh -u 1000 appuser",
            "USER appuser",
            "# 健康检查",
            "HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \\",
            "  CMD curl -f http://localhost/ || exit 1"
        ]
        
        if 'USER' not in self.optimized_content:
            self.optimized_content += '\n\n' + '\n'.join(security_lines)
            self.optimizations.append({
                'type': 'security',
                'message': '添加安全最佳实践',
                'additions': security_lines
            })
    
    def estimate_size_reduction(self) -> float:
        """估算大小减少"""
        original_lines = len(self.original_content.split('\n'))
        optimized_lines = len(self.optimized_content.split('\n'))
        
        # 简单估算
        reduction = (original_lines - optimized_lines) / original_lines * 100
        return max(0, reduction)

# 使用示例
def main():
    optimizer = DockerfileOptimizer('Dockerfile')
    report = optimizer.optimize_dockerfile()
    
    print("优化报告:")
    print(f"原始大小: {report['original_size']} 字符")
    print(f"优化后大小: {report['optimized_size']} 字符")
    print(f"预估减少: {report['estimated_reduction']:.2f}%")
    print("\n优化项目:")
    for opt in report['optimizations']:
        print(f"- {opt['message']}")

if __name__ == '__main__':
    main()

容器镜像最佳实践

镜像构建

  1. 多阶段构建: 分离构建环境和运行环境
  2. 层缓存优化: 合理安排指令顺序
  3. 基础镜像选择: 使用官方轻量镜像
  4. 清理缓存: 及时清理包管理器缓存

安全实践

  1. 最小权限: 使用非root用户运行
  2. 镜像扫描: 定期扫描安全漏洞
  3. 内容信任: 使用镜像签名验证
  4. 敏感信息: 避免硬编码敏感数据

存储优化

  1. 镜像清理: 定期清理无用镜像
  2. 分层存储: 利用共享层减少存储
  3. 压缩优化: 使用合适的压缩算法
  4. 垃圾回收: 实施自动垃圾回收

分发优化

  1. CDN加速: 使用内容分发网络
  2. 预拉取: 预先拉取常用镜像
  3. P2P分发: 考虑点对点分发
  4. 区域仓库: 部署区域镜像仓库

相关技能

  • docker-containerization - Docker容器化
  • kubernetes-basics - Kubernetes基础
  • ci-cd-pipeline - CI/CD流水线
  • security-audit - 安全审计

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the 容器镜像管理 AI skill do?

当管理容器镜像仓库时,分析镜像存储策略,优化镜像分发性能,解决镜像安全问题。验证镜像架构,设计CI/CD流水线,和最佳实践。

Why use 容器镜像管理 on TypingMind?

Because you install it once and use it with any model. 容器镜像管理 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 容器镜像管理 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microwind/ai-skills/tree/main/cloud-native/container-registry. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use 容器镜像管理?

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 容器镜像管理?

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

Is the 容器镜像管理 AI skill free?

Yes. It is published on GitHub by microwind under the MIT 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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