Docker Debugger Skill for Claude

Debug Docker containers, fix Dockerfile issues, optimize images, and troubleshoot docker-compose. Use when having Docker problems, container issues, or optimizing Docker builds.

791 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

235

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/OneWave-AI/claude-skills --skill docker-debugger

The instruction itself

10 sections, as written by the author

Docker Debugger

Instructions

When debugging Docker issues:

Identify the problem type: Build, runtime, networking, or performance
Gather information using diagnostic commands
Analyze logs and errors
Apply fixes

Diagnostic Commands

# Check running containers
docker ps -a

# View container logs
docker logs <container_id> --tail 100 -f

# Inspect container
docker inspect <container_id>

# Check resource usage
docker stats

# View container processes
docker top <container_id>

# Execute shell in running container
docker exec -it <container_id> /bin/sh

# Check Docker disk usage
docker system df

# View build history
docker history <image_name>

Common Issues & Solutions

1. Container exits immediately

# Check exit code
docker inspect <container_id> --format='{{.State.ExitCode}}'

# View last logs
docker logs <container_id>

Fixes:

Ensure CMD/ENTRYPOINT runs a foreground process
Check for missing dependencies
Verify environment variables

2. Build fails

# Use multi-stage builds for smaller images
FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM node:20-alpine AS runner
WORKDIR /app
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
CMD ["node", "dist/index.js"]

3. Networking issues

# List networks
docker network ls

# Inspect network
docker network inspect <network_name>

# Check container IP
docker inspect -f '{{range.NetworkSettings.Networks}}{{.IPAddress}}{{end}}' <container>

4. Volume permission issues

# Create non-root user
RUN addgroup -g 1001 -S appgroup && \
    adduser -u 1001 -S appuser -G appgroup

# Set ownership
COPY --chown=appuser:appgroup . .

USER appuser

Dockerfile Best Practices

# 1. Use specific tags, not :latest
FROM node:20.10-alpine

# 2. Set working directory
WORKDIR /app

# 3. Copy dependency files first (better caching)
COPY package*.json ./
RUN npm ci --only=production

# 4. Copy source after dependencies
COPY . .

# 5. Use non-root user
USER node

# 6. Set proper labels
LABEL maintainer="[email protected]"
LABEL version="1.0"

# 7. Use HEALTHCHECK
HEALTHCHECK --interval=30s --timeout=3s \
  CMD wget -q --spider http://localhost:3000/health || exit 1

# 8. Expose ports
EXPOSE 3000

# 9. Use exec form for CMD
CMD ["node", "server.js"]

docker-compose Debugging

# docker-compose.yml
services:
  app:
    build:
      context: .
      dockerfile: Dockerfile
    # Add for debugging
    stdin_open: true
    tty: true
    # Override command for debugging
    command: /bin/sh
    volumes:
      - .:/app
    environment:
      - DEBUG=true

# Rebuild without cache
docker-compose build --no-cache

# View logs
docker-compose logs -f app

# Restart single service
docker-compose restart app

Other skills for the same job

different authors, same section of the catalogue

Azure Kubernetes Automatic Readiness

by microsoft

vendor ×3

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.

17k tokens

How to use it

Copy the folder

Take onewave-ai/docker-debugger from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.