Back to Skills

create-multistage-dockerfile

pjt222
Updated 2 days ago
6 views
17
2
17
View on GitHub
Metaaidesign

About

This skill generates optimized multi-stage Dockerfiles that separate build and runtime environments to create minimal production images. It's designed for developers needing to reduce image size, remove build tools from final images, or deploy to constrained environments like serverless platforms. The skill covers builder/runtime stage separation, artifact copying, and various base image targets including scratch, distroless, and Alpine.

Quick Install

Claude Code

Recommended
Primary
npx skills add pjt222/agent-almanac -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternative
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/create-multistage-dockerfile

Copy and paste this command in Claude Code to install this skill

Documentation

造多階段 Dockerfile

建多階段 Dockerfile,以分建構與執行之環境,產最小之生產映像。

適用時機

  • 生產映像過大(編譯語言 >500MB)
  • 建構工具(編譯器、開發標頭)入終映像
  • 需自一 Dockerfile 產異之開發與生產映像
  • 部署至受限環境(邊緣、無伺服)

輸入

  • 必要:既存之 Dockerfile 或待容器化之項目
  • 必要:語言與建構系統(npm、pip、go build、cargo、maven)
  • 選擇性:目標執行基礎(slim、alpine、distroless、scratch)
  • 選擇性:終映像之大小預算

步驟

步驟一:辨建構與執行依賴

建構階段執行階段
編譯器gcc、g++、rustc不需
套件管理器npm、pip、cargo有時(直譯語言)
開發標頭-dev 套件不需
源程式全源樹僅編譯輸出
測試框架jest、pytest不需

步驟二:結構化多階段建構

核心模式:建於胖映像,複構件至 slim 映像。

# ---- Build Stage ----
FROM <build-image> AS builder
WORKDIR /src
COPY <dependency-manifest> .
RUN <install-dependencies>
COPY . .
RUN <build-command>

# ---- Runtime Stage ----
FROM <runtime-image>
COPY --from=builder /src/<artifact> /<dest>
EXPOSE <port>
CMD [<entrypoint>]

步驟三:施語言專屬模式

Node.js(剪之 node_modules)

FROM node:22-bookworm AS builder
WORKDIR /src
COPY package.json package-lock.json ./
RUN npm ci
COPY . .
RUN npm run build && npm prune --omit=dev

FROM node:22-bookworm-slim
RUN groupadd -r app && useradd -r -g app app
WORKDIR /app
COPY --from=builder /src/dist ./dist
COPY --from=builder /src/node_modules ./node_modules
COPY --from=builder /src/package.json .
USER app
EXPOSE 3000
CMD ["node", "dist/index.js"]

Python(virtualenv 複)

FROM python:3.12-bookworm AS builder
WORKDIR /src
RUN python -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .

FROM python:3.12-slim-bookworm
COPY --from=builder /opt/venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
WORKDIR /app
COPY --from=builder /src .
RUN groupadd -r app && useradd -r -g app app
USER app
EXPOSE 8000
CMD ["python", "app.py"]

Go(靜態二進制至 scratch)

FROM golang:1.23-bookworm AS builder
WORKDIR /src
COPY go.mod go.sum ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -ldflags="-s -w" -o /server ./cmd/server

FROM scratch
COPY --from=builder /etc/ssl/certs/ca-certificates.crt /etc/ssl/certs/
COPY --from=builder /server /server
EXPOSE 8080
ENTRYPOINT ["/server"]

Rust(靜態 musl 二進制)

FROM rust:1.82-bookworm AS builder
RUN apt-get update && apt-get install -y musl-tools && rm -rf /var/lib/apt/lists/*
RUN rustup target add x86_64-unknown-linux-musl
WORKDIR /src
COPY Cargo.toml Cargo.lock ./
RUN mkdir src && echo "fn main() {}" > src/main.rs \
    && cargo build --release --target x86_64-unknown-linux-musl \
    && rm -rf src
COPY . .
RUN touch src/main.rs && cargo build --release --target x86_64-unknown-linux-musl

FROM scratch
COPY --from=builder /src/target/x86_64-unknown-linux-musl/release/myapp /myapp
EXPOSE 8080
ENTRYPOINT ["/myapp"]

預期: 終映像僅含執行時與編譯構件。

失敗時:COPY --from=builder 路。用 docker build --target builder 除錯建構階段。

步驟四:擇執行基礎

基礎大小Shell用例
scratch0 MB靜態 Go/Rust 二進制
gcr.io/distroless/static~2 MB靜態二進制 + CA 憑證
gcr.io/distroless/base~20 MB動態二進制(libc)
*-slim50-150 MB直譯語言
alpine~7 MB需 shell 存取時

注: Alpine 用 musl libc。某 Python wheels 與 Node 原生模組或不可。直譯語言宜用 -slim(glibc)。

步驟五:跨階段之建構引數

ARG APP_VERSION=0.0.0

FROM golang:1.23 AS builder
ARG APP_VERSION
RUN go build -ldflags="-X main.version=${APP_VERSION}" -o /server .

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

建以:docker build --build-arg APP_VERSION=1.2.3 .

注: FROM 前之 ARG 為全域。各階段須重宣 ARG 以用之。

步驟六:比映像大小

# Build both variants
docker build -t myapp:fat --target builder .
docker build -t myapp:slim .

# Compare sizes
docker images --format "table {{.Repository}}\t{{.Tag}}\t{{.Size}}" | grep myapp

預期: 生產映像較建構階段小 50-90%。

驗證

  • docker build 各階段皆完
  • 終映像不含建構工具(編譯器、開發標頭)
  • docker run 於 slim 映像中行之無誤
  • 映像大小較單階段顯著減
  • COPY --from=builder 路正確
  • 無源程式洩入生產映像

常見陷阱

  • 缺執行庫:編譯程式或需共享庫(libclibssl)。詳測 slim 映像
  • COPY --from 路斷:構件路須完全合。用 docker build --target builder 繼以 docker run --rm builder ls /path 除錯
  • Alpine musl 問題:原生 Node.js 附加與某 Python 套件於 Alpine 敗。改用 -slim
  • 全域 ARG 範圍:宣於 FROM 前之 ARG 僅可用於 FROM 行。需用之階段內重宣
  • 忘 CA 憑證scratch 無憑證。自建構器複 /etc/ssl/certs/ca-certificates.crt 或用 distroless

相關技能

  • create-dockerfile - 單階段通用 Dockerfile
  • create-r-dockerfile - R 專屬之 Dockerfile,用 rocker 映像
  • optimize-docker-build-cache - 層快取與 BuildKit 功能
  • setup-compose-stack - 以多階段映像行之 compose 配置

GitHub Repository

pjt222/agent-almanac
Path: i18n/wenyan-lite/skills/create-multistage-dockerfile
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

Related Skills

content-collections

Meta

This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.

View skill

polymarket

Meta

This skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.

View skill

creating-opencode-plugins

Meta

This skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.

View skill

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill