Generate configuration files for applications, services, and infrastructure. Use when: (1) Setting up new projects (package.json, requirements.txt, tsconfig.json), (2) Creating Docker or Kubernetes configurations, (3) Configuring CI/CD pipelines (GitHub Actions, GitLab CI, CircleCI), (4) Setting up web servers (Nginx, Apache), (5) Defining infrastructure as code (Terraform, CloudFormation), (6) Generating linter/formatter configs (ESLint, Prettier, Black). Provides templates and custom-generated configs for diverse tech stacks.
npx skills add https://github.com/ArabelaTso/Skills-4-SE --skill configuration-generator
Generate configuration files for applications, services, and infrastructure across various formats and frameworks.
Access ready-to-use configuration templates from assets:
# Docker Compose
cat assets/docker-compose.yml
# Kubernetes
cat assets/kubernetes-deployment.yaml
# GitHub Actions
cat assets/github-actions-workflow.yml
Specify your requirements to get tailored configuration files.
Node.js (package.json)
{
"name": "my-app",
"version": "1.0.0",
"scripts": {
"start": "node index.js",
"dev": "nodemon index.js",
"test": "jest",
"build": "tsc"
},
"dependencies": {
"express": "^4.18.0"
},
"devDependencies": {
"typescript": "^5.0.0",
"jest": "^29.0.0"
}
}
Python (requirements.txt)
django==4.2.0
djangorestframework==3.14.0
psycopg2-binary==2.9.5
pytest==7.3.0
black==23.3.0
See app_configs.md for:
TypeScript (tsconfig.json)
{
"compilerOptions": {
"target": "ES2022",
"module": "commonjs",
"outDir": "./dist",
"rootDir": "./src",
"strict": true,
"esModuleInterop": true
},
"include": ["src/**/*"],
"exclude": ["node_modules", "dist"]
}
Dockerfile (Multi-stage)
FROM node:18-alpine as builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build
FROM node:18-alpine
WORKDIR /app
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
USER node
EXPOSE 3000
CMD ["node", "dist/index.js"]
Docker Compose - See assets/docker-compose.yml:
Deployment Configuration - See assets/kubernetes-deployment.yaml:
Example Deployment:
apiVersion: apps/v1
kind: Deployment
metadata:
name: web-app
spec:
replicas: 3
selector:
matchLabels:
app: web-app
template:
spec:
containers:
- name: web
image: myapp:latest
ports:
- containerPort: 8000
resources:
requests:
memory: "256Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "500m"
See infra_configs.md for:
GitHub Actions - See assets/github-actions-workflow.yml:
Example Workflow:
name: CI/CD Pipeline
on:
push:
branches: [ main ]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: '3.11'
- run: pip install -r requirements.txt
- run: pytest
What type of configuration do you need?
├─ Application Config
│ ├─ Package manager? → package.json, requirements.txt, Cargo.toml
│ ├─ Build tool? → tsconfig.json, webpack.config.js, vite.config.ts
│ ├─ Linter/formatter? → .eslintrc, .prettierrc, pyproject.toml
│ └─ Environment? → .env, .editorconfig, .gitignore
│
├─ Container/Orchestration
│ ├─ Docker? → Dockerfile, docker-compose.yml
│ └─ Kubernetes? → deployment.yaml, service.yaml, ingress.yaml
│
├─ Infrastructure as Code
│ ├─ Terraform? → main.tf, variables.tf, outputs.tf
│ └─ CloudFormation? → template.yaml
│
├─ CI/CD
│ ├─ GitHub Actions? → .github/workflows/ci.yml
│ ├─ GitLab CI? → .gitlab-ci.yml
│ └─ CircleCI? → .circleci/config.yml
│
└─ Web Server
├─ Nginx? → nginx.conf
└─ Apache? → httpd.conf, .htaccess
Use pre-built templates from assets/:
When to use:
How:
Example:
# Copy Docker Compose template
cp assets/docker-compose.yml ./
# Edit environment variables
# Deploy
docker-compose up -d
Generate from scratch based on specific needs:
When to use:
How:
Example Request: "Generate a GitHub Actions workflow for a Python Django app with PostgreSQL, Redis, and deployment to AWS ECS"
Generated Output: Custom workflow with:
Combine templates with customization:
When to use:
How:
Separate configuration from code:
Application:
# settings.py
import os
DATABASE_URL = os.getenv('DATABASE_URL')
SECRET_KEY = os.getenv('SECRET_KEY')
DEBUG = os.getenv('DEBUG', 'False') == 'True'
.env file:
DATABASE_URL=postgresql://localhost/mydb
SECRET_KEY=your-secret-key
DEBUG=true
Do commit:
.env.example (template)Don't commit:
.env (secrets).env.local (local overrides).gitignore:
.env
.env.local
.env.*.local
Check syntax before deploying:
# Docker Compose
docker-compose config
# Kubernetes
kubectl apply --dry-run=client -f deployment.yaml
# Terraform
terraform validate
# YAML syntax
yamllint config.yml
Add comments explaining purpose:
# docker-compose.yml
services:
web:
# Application server - handles HTTP requests
image: myapp:latest
ports:
# Expose on port 8000 for development
- "8000:8000"
environment:
# Database connection string
- DATABASE_URL=${DATABASE_URL}
Provide defaults for optional values:
# Kubernetes ConfigMap
apiVersion: v1
kind: ConfigMap
metadata:
name: app-config
data:
# Default to info level logging
LOG_LEVEL: "info"
# Default connection pool size
DB_POOL_SIZE: "10"
Development:
# docker-compose.dev.yml
services:
web:
build: .
volumes:
- ./app:/app # Hot reload
environment:
- DEBUG=true
Production:
# docker-compose.prod.yml
services:
web:
image: registry/myapp:latest
environment:
- DEBUG=false
deploy:
replicas: 3
Kubernetes Secrets:
apiVersion: v1
kind: Secret
metadata:
name: app-secrets
type: Opaque
stringData:
database-url: "postgresql://..."
api-key: "secret-key"
Docker Compose (with env file):
services:
web:
env_file:
- .env.production
Docker Compose:
services:
web:
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 40s
Kubernetes:
livenessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /ready
port: 8000
initialDelaySeconds: 5
periodSeconds: 5
Request: "Create complete configuration for a Python FastAPI application with PostgreSQL and Redis, using Docker Compose for development and Kubernetes for production"
Generated Configurations:
These files work together to provide:
YAML syntax errors:
# Check YAML syntax
python -c "import yaml; yaml.safe_load(open('config.yml'))"
# Or use yamllint
yamllint config.yml
Docker Compose issues:
# Validate and view resolved config
docker-compose config
# Check for errors
docker-compose config --quiet
Missing variables:
# List all required variables
grep -o '\${[^}]*}' docker-compose.yml
# Check if variable is set
echo $DATABASE_URL
Find process using port:
# Linux/macOS
lsof -i :8000
# Windows
netstat -ano | findstr :8000
See app_configs.md for complete templates:
See infra_configs.md for:
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 arabelatso/configuration-generator 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.
Without those the skill loads but fails at the first command.