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Agent skill

Performing Container Image Hardening

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Harden container images by minimizing attack surface, stripping unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations…

Apache-2.0Auto-check passedDevOps & Cloud

Install Performing Container Image Hardening

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-container-image-hardening -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-container-image-hardening --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/performing-container-image-hardening .claude/skills/performing-container-image-hardening && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
performing-container-image-hardening
GitHub stars
34k
Token cost
~2.1k tokens
SKILL.md length
444 words
Files
8 (incl. scripts, references, assets)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Harden container images by minimizing attack surface, stripping unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations…

  • Works in 6 steps: Use Multi-Stage Builds to Minimize Image… → Use Distroless Base Images → Remove Unnecessary Components → …
  • Building production container images
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls docker and trivy

What it does

Performing Container Image Hardening is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Harden container images by minimizing attack surface, stripping unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations to produce secure, production-ready images. Use when building production container images, when compliance requires CIS Docker Benchmark adherence, or when shrinking image size to reduce vulnerability exposure from unused packages.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/api-reference.md` and `references/standards.md`).

It sits in DevOps & Cloud, covering Containers. It works with Docker. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Building production container images
  • Compliance requires CIS Docker Benchmark adherence
  • Shrinking image size to reduce vulnerability exposure from unused packages

Example prompts

  • “/performing-container-image-hardening”

Requirements

  • Python 3
  • Node.js
  • Docker

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Use Multi-Stage Builds to Minimize Image Size
  2. Use Distroless Base Images
  3. Remove Unnecessary Components
  4. Configure Read-Only Filesystem
  5. Pin Base Image by Digest
  6. Validate Hardening with Automated Scanning

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • docker
    • trivy

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use docker, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Performing Container Image Hardening loads about 2.1k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 116 tokens; SKILL.md has 444 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~116
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 444 words, ~2,133 tokens.

Download SKILL.mdSave it as .claude/skills/performing-container-image-hardening/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
performing-container-image-hardening
description
Harden container images by minimizing attack surface, stripping unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations to produce secure, production-ready images. Use when building production container images, when compliance requires CIS Docker Benchmark adherence, or when shrinking image size to reduce vulnerability exposure from unused packages.
domain
cybersecurity
subdomain
devsecops
tags
devsecops, cicd, container-hardening, docker, cis-benchmark, secure-sdlc
version
1.0.0
author
mahipal
license
Apache-2.0
nist_csf
PR.PS-01, GV.SC-07, ID.IM-04, PR.PS-04
mitre_attack
T1195, T1554, T1059.004, T1610, T1611

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
dockerfile
# 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
dockerfile
# 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
dockerfile
# 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
yaml
# 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
dockerfile
# 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
bash
# 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
Show full SKILL.md (192 more words)Show less

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%

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references, assets) in skills/performing-container-image-hardening of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • assets/template.md
  • references/api-reference.md
  • references/standards.md
  • references/workflows.md
  • scripts/agent.py
  • scripts/process.py

Open the folder on GitHubat commit 54a7988

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Works with

Categories

Questions about Performing Container Image Hardening

What does Performing Container Image Hardening do?

Harden container images by minimizing attack surface, stripping unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations…. Performing Container Image Hardening is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Harden container images by minimizing attack surface, stripping unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations to produce secure, production-ready images.

When should I use Performing Container Image Hardening?

Performing Container Image Hardening fits situations like: building production container images; compliance requires CIS Docker Benchmark adherence; shrinking image size to reduce vulnerability exposure from unused packages.

How do I install Performing Container Image Hardening in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-container-image-hardening -a claude-code`. Or copy the skill folder (skills/performing-container-image-hardening in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/performing-container-image-hardening in your project. Claude Code loads it when a task matches its description.

How do I install Performing Container Image Hardening in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-container-image-hardening -a codex`. Or copy the skill folder (skills/performing-container-image-hardening in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/performing-container-image-hardening in your project. Codex loads it when a task matches its description.

Can I use Performing Container Image Hardening in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-container-image-hardening -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performing-container-image-hardening, .gemini/skills/performing-container-image-hardening, .github/skills/performing-container-image-hardening and .opencode/skills/performing-container-image-hardening in your project.

What does Performing Container Image Hardening need to run?

Going by SKILL.md and its folder, Performing Container Image Hardening needs Python for the scripts in its folder and the command-line tools its instructions call (docker and trivy). Our summary lists: Python 3; Node.js; Docker.

Does Performing Container Image Hardening access the network?

SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Performing Container Image Hardening safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Performing Container Image Hardening use?

Performing Container Image Hardening is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Performing Container Image Hardening use?

About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.

What are the alternatives to Performing Container Image Hardening?

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Who maintains Performing Container Image Hardening?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.