aj-geddes/useful-ai-prompts/artifact-management

Artifact Management

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Implement comprehensive artifact management strategies for storing, versioning, and distributing built binaries, Docker images, and packages across environments.

When to Use

• Docker image registry management
• Package publication and versioning
• Build artifact storage and retrieval
• Container image optimization
• Artifact retention policies
• Multi-registry distribution
• Dependency caching

Quick Start

Minimal working example:

# Dockerfile with multi-stage build for optimization
FROM node:18-alpine AS dependencies
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
 
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build
 
FROM node:18-alpine AS runtime
WORKDIR /app
COPY --from=dependencies /app/node_modules ./node_modules
COPY --from=builder /app/dist ./dist
COPY package*.json ./
 
EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=3s --start-period=40s --retries=3 \
  CMD node healthcheck.js
 
CMD ["node", "dist/server.js"]
 
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Best Practices

✅ DO

• Use semantic versioning for artifacts
• Implement image scanning before deployment
• Set retention policies for old artifacts
• Use multi-stage builds for Docker images
• Sign and verify artifacts
• Implement artifact immutability
• Document artifact metadata
• Use specific base image versions
• Implement vulnerability scanning
• Cache layers aggressively
• Tag images with commit SHA
• Compress artifacts for storage

❌ DON'T

• Use latest tag as sole identifier
• Store secrets in artifacts
• Push artifacts without scanning
• Use untrusted base images
• Skip artifact verification
• Overwrite published artifacts
• Mix binary and source artifacts
• Ignore image layer optimization
• Store build logs with sensitive data