本文へ移動
cccskills
無料GitHub で公開

docker-multi-stage-optimizer

Multi-stage Docker build optimizer for minimal, secure production images. Activate on: Dockerfile optimization, multi-stage build, distroless image, container size reduction, Docker security scanning, BuildKit features. NOT for: container orchestration (use kubernetes-manifest-generator), CI/CD pipelines (use github-actions-pipeline-builder), runtime container config (use environment-config-manager).

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.8 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Docker Multi-Stage Optimizer

Expert in crafting minimal, secure Docker images using multi-stage builds, distroless bases, and BuildKit optimizations.

Activation Triggers

Activate on: "Dockerfile optimization", "multi-stage build", "distroless image", "container size", "Docker security scan", "BuildKit", "image layers", "slim image", "Docker best practices"

NOT for: Container orchestration → kubernetes-manifest-generator | CI/CD pipelines → github-actions-pipeline-builder | Runtime config → environment-config-manager

Quick Start

  1. Audit the existing Dockerfile — identify redundant layers, large base images, leaked secrets
  2. Design multi-stage pipeline — separate build, test, and runtime stages
  3. Select minimal base — distroless, alpine, or scratch depending on runtime needs
  4. Enable BuildKit — use cache mounts, secret mounts, and parallel builds
  5. Scan and validate — run Trivy/Grype, verify no dev dependencies in final image

Core Capabilities

DomainTechnologies
Multi-Stage BuildsBuilder pattern, named stages, COPY --from, cross-compilation
Base Imagesgcr.io/distroless, alpine 3.21, chainguard, scratch
BuildKitCache mounts, secret mounts, SSH mounts, heredocs, parallel stages
SecurityTrivy, Grype, Syft SBOM, non-root USER, read-only filesystem
Size OptimizationLayer squashing, .dockerignore, multi-arch builds, UPX compression

Architecture Patterns

Multi-Stage Build Pipeline

# Stage 1: Dependencies (cached aggressively)
FROM node:22-alpine AS deps
WORKDIR /app
COPY package.json pnpm-lock.yaml ./
RUN --mount=type=cache,target=/root/.local/share/pnpm/store \
    pnpm install --frozen-lockfile

# Stage 2: Build
FROM deps AS build
COPY . .
RUN pnpm build

# Stage 3: Production (minimal)
FROM gcr.io/distroless/nodejs22-debian12 AS production
COPY --from=build /app/dist /app
COPY --from=deps /app/node_modules /app/node_modules
USER nonroot
EXPOSE 3000
CMD ["app/server.js"]

BuildKit Cache Mount Pattern

# Python: cache pip downloads across builds
RUN --mount=type=cache,target=/root/.cache/pip \
    pip install -r requirements.txt

# Go: cache module downloads and build cache
RUN --mount=type=cache,target=/go/pkg/mod \
    --mount=type=cache,target=/root/.cache/go-build \
    go build -o /app ./cmd/server

Security-First Layer Ordering

Least-changing layers first (maximize cache):
  1. Base image selection
  2. System packages (apt-get)
  3. Dependency lockfiles (package-lock, go.sum)
  4. Dependency install
  5. Source code COPY
  6. Build command
  7. Runtime stage (minimal)

Anti-Patterns

  1. Single-stage production images — shipping compilers, dev tools, and test fixtures in production. Always use multi-stage to separate build from runtime.
  2. Using latest tag for base images — non-reproducible builds. Pin to digest or specific version (node:22.14-alpine3.21).
  3. Running as root — containers default to root. Always add USER nonroot or USER 1000:1000 in the final stage.
  4. COPY . . before dependency install — busts the dependency cache on every source change. Copy lockfiles first, install, then copy source.
  5. Secrets in build args — ARG/ENV values persist in image layers. Use --mount=type=secret with BuildKit instead.

Quality Checklist

[ ] Final image uses distroless, alpine, or scratch base
[ ] No compiler, build tools, or dev dependencies in final stage
[ ] Image runs as non-root user
[ ] No secrets in build args or environment variables
[ ] .dockerignore excludes .git, node_modules, .env files
[ ] Base image pinned to specific version (not :latest)
[ ] Trivy scan passes with zero critical/high CVEs
[ ] BuildKit cache mounts used for package managers
[ ] HEALTHCHECK instruction defined
[ ] Image size under target (Node: <150MB, Go: <30MB, Python: <200MB)
[ ] Multi-arch build tested (amd64 + arm64)
[ ] SBOM generated with Syft

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月8日 更新

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月8日 更新

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月8日 更新

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月8日 更新

Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月8日 更新

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月8日 更新

curiositech のスキルをすべて見る

このスキルの問題を報告する