Devcontainer Agent Skill
Diagnose devcontainer configuration problems and guide development environment setup. This skill should be used when the user asks to 'set up devcontainer', 'fix container startup', 'configure VS Code dev container', 'Codespaces setup', or has Docker development environment issues. Keywords: devcontainer, docker, VS Code, Codespaces, container, development environment, Dockerfile.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/jwynia/agent-skills --skill devcontainerWhat comes with it
66 375 bytes besides the instructionThe instruction itself
18 sections, as written by the authorDevcontainer Diagnostic
Diagnose devcontainer and Docker development environment problems. Help create reproducible, fast-starting development environments that work consistently across VS Code, GitHub Codespaces, and team members.
When to Use This Skill
Use this skill when:
- Setting up a new devcontainer
- Container startup is too slow
- Configuration errors or conflicts
- Different behavior in VS Code vs Codespaces
- Multi-service development environment needed
Do NOT use this skill when:
- Writing application code
- Deploying to production
- Configuring CI/CD pipelines
Core Principle
Development containers should provide instant productivity. Every configuration choice affects startup time, reproducibility, and team onboarding. Make these trade-offs explicit.
Diagnostic States
DV0: No Devcontainer Strategy
Symptoms: Manual setup, "check the README", works on one machine fails on others
Interventions:
- Start with pre-built devcontainer base image
- Use
assets/devcontainer-simple.mdtemplate
DV1: Slow Container Startup
Symptoms: 5+ minute startup, heavy postCreateCommand, avoiding rebuilds
Key Questions:
- How long does startup actually take?
- What's in postCreateCommand?
- Are you using prebuilds?
Interventions:
- Move npm install/pip install to Dockerfile (cached)
- Use mcr.microsoft.com/devcontainers/* base images
- Configure prebuilds for team repos
- Run
scripts/analyze-devcontainer.ts
DV2: Configuration Problems
Symptoms: JSON errors, VS Code won't connect, features conflicting
Checklist:
- [ ] devcontainer.json passes JSON validation
- [ ] Using only ONE of: image, build.dockerfile, dockerComposeFile
- [ ] Features are compatible and ordered correctly
- [ ] Extensions use correct publisher.extension-name format
DV3: Environment Parity Issues
Symptoms: Works in VS Code, fails in Codespaces (or vice versa)
Common Issues:
| Issue | Local VS Code | Codespaces |
|-------|---------------|------------|
| Docker socket | Usually available | Docker-in-Docker needed |
| Secrets | .env files work | Use Codespaces secrets |
| File watching | Native | May need polling |
DV4: Multi-Service Complexity
Symptoms: Need database/cache/queue, services can't communicate
Interventions:
- Use Docker Compose integration
- Named volumes for persistence
- Health checks for service readiness
- Use
assets/devcontainer-compose.mdtemplate
DV5: Dockerfile Issues
Symptoms: Build failures, huge images, no caching
Best Practices:
FROM mcr.microsoft.com/devcontainers/base:ubuntu
# Dependencies first (cached)
RUN apt-get update && apt-get install -y \
build-essential && rm -rf /var/lib/apt/lists/*
# Copy deps then install (cached if deps unchanged)
COPY package*.json ./
RUN npm install
# Code last (changes frequently)
COPY . .
DV6: Devcontainer Validated
Indicators:
- Startup under 2 minutes
- Works in VS Code and Codespaces
- New developers productive in 30 minutes
Available Scripts
| Script | Purpose | Usage |
|--------|---------|-------|
| analyze-devcontainer.ts | Find issues and optimizations | deno run --allow-read scripts/analyze-devcontainer.ts |
| validate-dockerfile.ts | Check Dockerfile best practices | deno run --allow-read scripts/validate-dockerfile.ts |
| scan-image.ts | Vulnerability scanning (wraps Trivy) | deno run --allow-run scripts/scan-image.ts [image] |
Anti-Patterns
The Kitchen Sink
Installing every tool "just in case" - 10+ minute startups.
Fix: Start minimal. Add only when needed.
The postCreateCommand Overload
Everything in postCreateCommand - runs every time.
Fix: Move stable operations to Dockerfile.
The Snowflake Container
Manual changes inside running containers.
Fix: ALL changes go in config files.
Templates
assets/devcontainer-simple.md- Basic single-container setupassets/devcontainer-dockerfile.md- Custom Dockerfile approachassets/devcontainer-compose.md- Multi-service setup
Related Skills
- system-design - Multi-service architecture informs Compose config
- pwa-development - Consistent environment for PWA toolchain
Other skills for the same job
different authors, same section of the catalogue
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.
How to use it
Take jwynia/devcontainer 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 pip, npm, apt.
Without those the skill loads but fails at the first command.