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Agent Skill · 图像处理

performing-container-image-hardening(执行容器镜像强化)

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。

performing-container-image-hardening 是图像处理类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描已通过,按安装量在图像处理类的 1,562 个 Skill 里排第 1,537。

总安装 192周安装 8GitHub Stars 5,881下载 64核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:performing-container-image-hardening(执行容器镜像强化)
来源仓库:https://github.com/mukul975/anthropic-cybersecurity-skills
仓库路径:skills/performing-container-image-hardening
安装命令:
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performing-container-image-hardening
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performing-container-image-hardening

这个 Skill 是做什么的

图像处理类 Skill 处理生成、裁剪、压缩和批量转换图片的任务。

用于辅助容器镜像强化和图像处理工作流。适合在 Codex、Claude、Cursor、Gemini CLI 中根据文本生成图片或处理视觉提示词。使用时需确认输入图片、版权来源和输出格式,避免越权操作。涉及人物或品牌素材时,应额外核对授权与内容合规性。

performing-container-image-hardening 属于图像处理类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/performing-container-image-hardening

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skil…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

performing-container-image-hardening 怎么安装?

在终端执行 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performi…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

performing-container-image-hardening 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

performing-container-image-hardening 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performing-container-image-hardening 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

performing-container-image-hardening 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

performing-container-image-hardening 用的人多吗?

公开数据是累计安装 192 次,近一周安装 8 次,下载 64 次,GitHub 5,881 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

performing-container-image-hardening 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 5,881 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理图像处理类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

图像处理类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
ai-image-generation

AI 图像生成 · Codex、Claude

4,365,552382调用外部服务未明确有提醒
openai-image-gen

OpenAI 图像生成 · OpenClaw

1,906,88432需要联网MIT-0有提醒
image

图像 · OpenClaw

525,99926需要联网MIT-0通过
baoyu-cover-image

宝玉封面图 · Codex、Claude

474,24016,779只读MIT有提醒
baoyu-image-gen

宝玉图像生成器 · Codex、Claude

468,00716,778接触敏感数据MIT通过
performing-container-image-hardening

执行容器镜像强化 · 本页

1925,881执行命令未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Performing Container Image Hardening

When to Use

  • When building production container images that need minimal attack surface
  • When compliance requires CIS Docker Benchmark adherence for container configurations
  • When reducing image size to minimize vulnerability exposure from unused packages
  • When implementing defense-in-depth for containerized workloads
  • When migrating from fat base images to distroless or minimal images

Do not use for runtime container security monitoring (use Falco), for host-level Docker daemon hardening (use CIS Docker Benchmark host checks), or for container orchestration security (use Kubernetes security scanning).

Prerequisites

  • Docker or BuildKit for multi-stage builds
  • Base image options: distroless, Alpine, slim, or scratch
  • Container scanning tool (Trivy) for validation
  • CIS Docker Benchmark reference

Workflow

Step 1: Use Multi-Stage Builds to Minimize Image Size

# Build stage with all dependencies
FROM python:3.12-bookworm AS builder
WORKDIR /build
COPY requirements.txt .
RUN pip install --no-cache-dir --prefix=/install -r requirements.txt
COPY src/ ./src/
RUN python -m compileall src/

# Production stage with minimal base
FROM python:3.12-slim-bookworm AS production
RUN apt-get update && \
    apt-get install -y --no-install-recommends libpq5 && \
    rm -rf /var/lib/apt/lists/* && \
    apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false

COPY --from=builder /install /usr/local
COPY --from=builder /build/src /app/src

RUN groupadd -r appuser && useradd -r -g appuser -d /app -s /sbin/nologin appuser
RUN chown -R appuser:appuser /app

USER appuser
WORKDIR /app

HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
  CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8080/health')" || exit 1

EXPOSE 8080
ENTRYPOINT ["python", "-m", "src.main"]

Step 2: Use Distroless Base Images

# Go application with distroless
FROM golang:1.22 AS builder
WORKDIR /app
COPY go.* ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -ldflags="-w -s" -o /server .

FROM gcr.io/distroless/static-debian12:nonroot
COPY --from=builder /server /server
USER nonroot:nonroot
ENTRYPOINT ["/server"]

Step 3: Remove Unnecessary Components

# Hardened image checklist
FROM ubuntu:24.04 AS base

RUN apt-get update && \
    apt-get install -y --no-install-recommends \
      ca-certificates \
      libssl3 && \
    # Remove package manager to prevent runtime package installation
    apt-get purge -y --auto-remove apt dpkg && \
    rm -rf /var/lib/apt/lists/* \
           /var/cache/apt/* \
           /tmp/* \
           /var/tmp/* \
           /usr/share/doc/* \
           /usr/share/man/* \
           /usr/share/info/* \
           /root/.cache

# Remove shells if not needed
RUN rm -f /bin/sh /bin/bash /usr/bin/sh 2>/dev/null || true

# Remove setuid/setgid binaries
RUN find / -perm /6000 -type f -exec chmod a-s {} + 2>/dev/null || true

Step 4: Configure Read-Only Filesystem

# Kubernetes deployment with read-only root filesystem
apiVersion: apps/v1
kind: Deployment
metadata:
  name: hardened-app
spec:
  template:
    spec:
      securityContext:
        runAsNonRoot: true
        runAsUser: 65534
        fsGroup: 65534
        seccompProfile:
          type: RuntimeDefault
      containers:
        - name: app
          image: app:hardened
          securityContext:
            allowPrivilegeEscalation: false
            readOnlyRootFilesystem: true
            capabilities:
              drop: ["ALL"]
          volumeMounts:
            - name: tmp
              mountPath: /tmp
            - name: cache
              mountPath: /app/cache
      volumes:
        - name: tmp
          emptyDir:
            sizeLimit: 100Mi
        - name: cache
          emptyDir:
            sizeLimit: 50Mi

Step 5: Pin Base Image by Digest

# Pin to exact image digest for reproducibility
FROM python:3.12-slim-bookworm@sha256:abcdef1234567890 AS production
# This ensures the exact same base image is used every time

Step 6: Validate Hardening with Automated Scanning

# Scan hardened image with Trivy
trivy image --severity HIGH,CRITICAL hardened-app:latest

# Check CIS Docker Benchmark compliance
docker run --rm -v /var/run/docker.sock:/var/run/docker.sock \
  aquasec/docker-bench-security

# Verify no root processes
docker run --rm hardened-app:latest whoami
# Expected: appuser (NOT root)

# Verify read-only filesystem
docker run --rm hardened-app:latest touch /test 2>&1
# Expected: Read-only file system error

Key Concepts

TermDefinition
Multi-Stage BuildDocker build technique using multiple FROM stages to separate build and runtime, reducing final image size
DistrolessGoogle-maintained minimal container images containing only the application and runtime dependencies
Non-Root UserRunning container processes as unprivileged user to limit impact of container escape exploits
Read-Only RootMounting the container root filesystem as read-only to prevent runtime modification
Image DigestSHA256 hash uniquely identifying an exact image version, more precise than mutable tags
Scratch ImageEmpty Docker base image used for statically compiled binaries requiring no OS
Security ContextKubernetes pod/container-level security settings controlling privileges, filesystem, and capabilities

Tools & Systems

  • Docker BuildKit: Advanced Docker build engine supporting multi-stage builds and build secrets
  • Distroless Images: Google's minimal container base images (static, base, java, python, nodejs)
  • docker-bench-security: Script checking CIS Docker Benchmark compliance
  • Trivy: Container image vulnerability and misconfiguration scanner
  • Hadolint: Dockerfile linter enforcing best practices

Common Scenarios

Scenario: Reducing a 1.2GB Python Image to Under 150MB

Context: A data science team uses python:3.12 as base image (1.2GB) with scientific computing packages. The image has 200+ known CVEs from unnecessary system packages.

Approach:

  1. Switch to python:3.12-slim-bookworm as base (150MB) and install only required system libraries
  2. Use multi-stage build: compile C extensions in builder stage, copy wheels to production
  3. Pin numpy, pandas, and scipy to pre-built wheels to avoid build dependencies in production
  4. Remove pip, setuptools, and wheel from the final image
  5. Create non-root user and set filesystem permissions
  6. Validate with Trivy: expect CVE count to drop from 200+ to under 20

Pitfalls: Some Python packages require shared libraries at runtime (libgomp, libstdc++). Test the application thoroughly after removing system packages. Alpine-based images use musl libc which can cause compatibility issues with numpy and pandas.

Output Format

Container Image Hardening Report
==================================
Image: app:hardened
Base: python:3.12-slim-bookworm
Date: 2026-02-23

SIZE COMPARISON:
  Before hardening: 1,247 MB (python:3.12)
  After hardening:  143 MB  (python:3.12-slim + multi-stage)
  Reduction: 88.5%

SECURITY CHECKS:
  [PASS] Non-root user configured (appuser:1000)
  [PASS] HEALTHCHECK instruction present
  [PASS] No setuid/setgid binaries found
  [PASS] Package manager removed
  [PASS] Base image pinned by digest
  [PASS] No shell access (/bin/sh removed)
  [WARN] /tmp writable (emptyDir mounted)

VULNERABILITY COMPARISON:
  Before: 234 CVEs (12 Critical, 45 High)
  After:  18 CVEs (0 Critical, 3 High)
  Reduction: 92.3%

平台分布

Codex

35.55%
按下载量换算23

Claude

28.56%
按下载量换算18

Cursor

18.81%
按下载量换算12

Gemini CLI

9.63%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performing-container-image-hardening 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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