Use for Docker with Bun, Dockerfiles, oven/bun image, containerization, and deployments.
npx skills add https://github.com/secondsky/claude-skills --skill bun-docker
Deploy Bun applications in Docker containers using official images.
# Latest stable
docker pull oven/bun
# Specific version
docker pull oven/bun:1.0.0
# Variants
oven/bun:latest # Full image (~100MB)
oven/bun:slim # Minimal image (~80MB)
oven/bun:alpine # Alpine-based (~50MB)
oven/bun:distroless # Distroless (~60MB)
oven/bun:debian # Debian-based (~100MB)
FROM oven/bun:1 AS base
WORKDIR /app
# Install dependencies
COPY package.json bun.lockb ./
RUN bun install --frozen-lockfile
# Copy source
COPY . .
# Run
EXPOSE 3000
CMD ["bun", "run", "src/index.ts"]
# Build stage
FROM oven/bun:1 AS builder
WORKDIR /app
COPY package.json bun.lockb ./
RUN bun install --frozen-lockfile
COPY . .
RUN bun run build
# Production stage
FROM oven/bun:1-slim AS production
WORKDIR /app
# Copy only production dependencies
COPY package.json bun.lockb ./
RUN bun install --frozen-lockfile --production
# Copy built assets
COPY --from=builder /app/dist ./dist
# Run as non-root
USER bun
EXPOSE 3000
CMD ["bun", "run", "dist/index.js"]
FROM oven/bun:1-alpine
WORKDIR /app
# Alpine uses apk for packages
RUN apk add --no-cache git
COPY package.json bun.lockb ./
RUN bun install --frozen-lockfile
COPY . .
CMD ["bun", "run", "src/index.ts"]
# Build stage
FROM oven/bun:1 AS builder
WORKDIR /app
COPY . .
RUN bun install --frozen-lockfile
RUN bun build src/index.ts --compile --outfile=app
# Runtime stage
FROM gcr.io/distroless/base
COPY --from=builder /app/app /app
ENTRYPOINT ["/app"]
# docker-compose.yml
version: "3.8"
services:
app:
build: .
ports:
- "3000:3000"
environment:
- NODE_ENV=production
- DATABASE_URL=postgres://db:5432/app
depends_on:
- db
restart: unless-stopped
db:
image: postgres:16-alpine
environment:
POSTGRES_DB: app
POSTGRES_USER: user
POSTGRES_PASSWORD: password
volumes:
- postgres_data:/var/lib/postgresql/data
volumes:
postgres_data:
# docker-compose.dev.yml
version: "3.8"
services:
app:
build:
context: .
dockerfile: Dockerfile.dev
ports:
- "3000:3000"
volumes:
- ./src:/app/src
- ./package.json:/app/package.json
command: bun --hot run src/index.ts
# Dockerfile.dev
FROM oven/bun:1
WORKDIR /app
COPY package.json bun.lockb ./
RUN bun install
# Source mounted as volume
CMD ["bun", "--hot", "run", "src/index.ts"]
FROM oven/bun:1 AS builder
WORKDIR /app
COPY . .
RUN bun install --frozen-lockfile
RUN bun build src/index.ts --compile --outfile=server
# Minimal runtime
FROM ubuntu:22.04
# Install runtime dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
ca-certificates \
&& rm -rf /var/lib/apt/lists/*
COPY --from=builder /app/server /usr/local/bin/server
USER nobody
EXPOSE 3000
CMD ["server"]
FROM oven/bun:1
WORKDIR /app
COPY package.json bun.lockb ./
RUN bun install --frozen-lockfile
COPY . .
# Create data directory
RUN mkdir -p /app/data
# Volume for SQLite database
VOLUME /app/data
ENV DATABASE_PATH=/app/data/app.sqlite
CMD ["bun", "run", "src/index.ts"]
FROM oven/bun:1
WORKDIR /app
COPY . .
RUN bun install --frozen-lockfile
EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
CMD curl -f http://localhost:3000/health || exit 1
CMD ["bun", "run", "src/index.ts"]
// Health endpoint
app.get("/health", (c) => c.json({ status: "ok" }));
FROM oven/bun:1
WORKDIR /app
# Build-time args
ARG NODE_ENV=production
ARG API_URL
# Runtime env
ENV NODE_ENV=${NODE_ENV}
ENV API_URL=${API_URL}
COPY . .
RUN bun install --frozen-lockfile
CMD ["bun", "run", "src/index.ts"]
# Build with args
docker build --build-arg API_URL=https://api.example.com -t myapp .
# Run with env
docker run -e DATABASE_URL=postgres://... myapp
FROM oven/bun:1 AS base
WORKDIR /app
# Cache dependencies separately
FROM base AS deps
COPY package.json bun.lockb ./
RUN bun install --frozen-lockfile
# Build
FROM deps AS builder
COPY . .
RUN bun run build
# Production
FROM base AS runner
COPY --from=deps /app/node_modules ./node_modules
COPY --from=builder /app/dist ./dist
COPY package.json ./
USER bun
CMD ["bun", "run", "dist/index.js"]
When installing packages in Docker builds, follow supply chain security best practices:
trustedDependenciespackage.json for reproducible buildssocket package score npm <pkg> to check packages before they reach your imageLoad the dependency-upgrade skill for full security configuration including Socket CLI integration, cooldown setup, lockfile validation, and CI enforcement.
FROM oven/bun:1-slim
WORKDIR /app
# Don't run as root
USER bun
# Copy with correct ownership
COPY --chown=bun:bun package.json bun.lockb ./
RUN bun install --frozen-lockfile --production
COPY --chown=bun:bun . .
# Read-only filesystem
# (use with: docker run --read-only)
EXPOSE 3000
CMD ["bun", "run", "src/index.ts"]
node_modules
.git
.gitignore
*.md
Dockerfile*
docker-compose*
.env*
.DS_Store
coverage
dist
.bun
# Build
docker build -t myapp .
# Run
docker run -p 3000:3000 myapp
# Run with env file
docker run --env-file .env -p 3000:3000 myapp
# Interactive shell
docker run -it oven/bun sh
# Check Bun version
docker run oven/bun bun --version
| Error | Cause | Fix |
|-------|-------|-----|
| bun.lockb not found | Missing lockfile | Run bun install locally |
| EACCES permission | File ownership | Use --chown=bun:bun |
| OOM killed | Memory limit | Increase container memory |
| No space left | Large layers | Use multi-stage builds |
Load references/optimization.md when:
Load references/kubernetes.md when:
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take secondsky/bun-docker from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference apt, docker.
Without those the skill loads but fails at the first command.