Configuration Generator logo

Configuration Generator

Community
ArabelaTso
configuration-generator

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.

Overview

PublisherArabelaTso
RepositorySkills-4-SE
Skill nameconfiguration-generator
Stars
258
Forks
25
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ArabelaTso on GitHub. Read the source before you install it.

Installation

Install the Configuration Generator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/configuration-generator
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git /tmp/Skills-4-SE
mkdir -p .claude/skills
cp -r /tmp/Skills-4-SE/skills/configuration-generator .claude/skills/configuration-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Configuration Generator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Configuration Generator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Configuration Generator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Configuration Generator

Generate configuration files for applications, services, and infrastructure across various formats and frameworks.

Quick Start

Use Built-in Templates

Access ready-to-use configuration templates from assets:

bash
# Docker Compose
cat assets/docker-compose.yml

# Kubernetes
cat assets/kubernetes-deployment.yaml

# GitHub Actions
cat assets/github-actions-workflow.yml

Generate Custom Configuration

Specify your requirements to get tailored configuration files.

Common Configuration Types

Application Configurations

Package Managers

Node.js (package.json)

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:

  • Package managers (npm, pip, cargo)
  • Build tools (TypeScript, Webpack, Vite)
  • Linters/formatters (ESLint, Prettier, Black)
  • Testing frameworks (Jest, pytest)
  • Environment files (.env, .editorconfig, .gitignore)
Build Configurations

TypeScript (tsconfig.json)

json
{
  "compilerOptions": {
    "target": "ES2022",
    "module": "commonjs",
    "outDir": "./dist",
    "rootDir": "./src",
    "strict": true,
    "esModuleInterop": true
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules", "dist"]
}

Infrastructure Configurations

Docker

Dockerfile (Multi-stage)

dockerfile
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:

  • Multi-service setup (web, database, cache)
  • Environment variable configuration
  • Volume management
  • Network configuration
Kubernetes

Deployment Configuration - See assets/kubernetes-deployment.yaml:

  • Deployment with replicas
  • ConfigMap and Secret management
  • Service and Ingress configuration
  • Health probes
  • Resource limits

Example Deployment:

yaml
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:

  • Docker and Kubernetes
  • Terraform (AWS, GCP, Azure)
  • CI/CD pipelines
  • Web servers (Nginx, Apache)
CI/CD Pipelines

GitHub Actions - See assets/github-actions-workflow.yml:

  • Test, build, and deploy workflow
  • Service containers (PostgreSQL, Redis)
  • Docker image building
  • Deployment automation

Example Workflow:

yaml
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

Configuration Decision Tree

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

Generation Approaches

Approach 1: Template-Based

Use pre-built templates from assets/:

When to use:

  • Standard setups
  • Quick prototyping
  • Learning/reference

How:

  1. Identify needed template
  2. Copy from assets
  3. Customize variables
  4. Deploy

Example:

bash
# Copy Docker Compose template
cp assets/docker-compose.yml ./

# Edit environment variables
# Deploy
docker-compose up -d

Approach 2: Requirements-Based Generation

Generate from scratch based on specific needs:

When to use:

  • Custom requirements
  • Complex setups
  • Specific tech stack

How:

  1. Specify requirements
  2. Choose tech stack
  3. Define constraints
  4. Generate configuration

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:

  • Python 3.11 setup
  • PostgreSQL and Redis services
  • Django test suite
  • Docker image build
  • AWS ECS deployment

Approach 3: Hybrid

Combine templates with customization:

When to use:

  • Partially standard setup
  • Template needs modification
  • Best practices + customization

How:

  1. Start with template
  2. Identify customization points
  3. Modify specific sections
  4. Validate configuration

Best Practices

1. Use Environment Variables

Separate configuration from code:

Application:

python
# settings.py
import os

DATABASE_URL = os.getenv('DATABASE_URL')
SECRET_KEY = os.getenv('SECRET_KEY')
DEBUG = os.getenv('DEBUG', 'False') == 'True'

.env file:

bash
DATABASE_URL=postgresql://localhost/mydb
SECRET_KEY=your-secret-key
DEBUG=true

2. Version Control Best Practices

Do commit:

  • .env.example (template)
  • Configuration templates
  • Development configs

Don't commit:

  • .env (secrets)
  • .env.local (local overrides)
  • Production credentials

.gitignore:

.env
.env.local
.env.*.local

3. Validate Configurations

Check syntax before deploying:

bash
# Docker Compose
docker-compose config

# Kubernetes
kubectl apply --dry-run=client -f deployment.yaml

# Terraform
terraform validate

# YAML syntax
yamllint config.yml

4. Document Configuration

Add comments explaining purpose:

yaml
# 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}

5. Use Sensible Defaults

Provide defaults for optional values:

yaml
# 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"

Common Patterns

Multi-Environment Configuration

Development:

yaml
# docker-compose.dev.yml
services:
  web:
    build: .
    volumes:
      - ./app:/app  # Hot reload
    environment:
      - DEBUG=true

Production:

yaml
# docker-compose.prod.yml
services:
  web:
    image: registry/myapp:latest
    environment:
      - DEBUG=false
    deploy:
      replicas: 3

Secret Management

Kubernetes Secrets:

yaml
apiVersion: v1
kind: Secret
metadata:
  name: app-secrets
type: Opaque
stringData:
  database-url: "postgresql://..."
  api-key: "secret-key"

Docker Compose (with env file):

yaml
services:
  web:
    env_file:
      - .env.production

Health Checks

Docker Compose:

yaml
services:
  web:
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

Kubernetes:

yaml
livenessProbe:
  httpGet:
    path: /health
    port: 8000
  initialDelaySeconds: 30
  periodSeconds: 10
readinessProbe:
  httpGet:
    path: /ready
    port: 8000
  initialDelaySeconds: 5
  periodSeconds: 5

Complete Example

Request: "Create complete configuration for a Python FastAPI application with PostgreSQL and Redis, using Docker Compose for development and Kubernetes for production"

Generated Configurations:

  1. Dockerfile
  2. docker-compose.yml (development)
  3. requirements.txt
  4. kubernetes/deployment.yaml (production)
  5. .env.example
  6. .github/workflows/deploy.yml

These files work together to provide:

  • Local development environment
  • CI/CD pipeline
  • Production deployment
  • Secret management
  • Health monitoring

Troubleshooting

Configuration Validation Errors

YAML syntax errors:

bash
# Check YAML syntax
python -c "import yaml; yaml.safe_load(open('config.yml'))"

# Or use yamllint
yamllint config.yml

Docker Compose issues:

bash
# Validate and view resolved config
docker-compose config

# Check for errors
docker-compose config --quiet

Environment Variable Issues

Missing variables:

bash
# List all required variables
grep -o '\${[^}]*}' docker-compose.yml

# Check if variable is set
echo $DATABASE_URL

Port Conflicts

Find process using port:

bash
# Linux/macOS
lsof -i :8000

# Windows
netstat -ano | findstr :8000

Reference Materials

Application Configs

See app_configs.md for complete templates:

  • Package managers (package.json, requirements.txt, Pipfile, Cargo.toml)
  • Build tools (tsconfig.json, webpack.config.js, vite.config.ts)
  • Linters and formatters (.eslintrc, .prettierrc, .flake8, pyproject.toml)
  • Testing frameworks (jest.config.js, pytest.ini)
  • Environment files (.env.example, .editorconfig, .gitignore)

Infrastructure Configs

See infra_configs.md for:

  • Docker (Dockerfile multi-stage, docker-compose.yml, .dockerignore)
  • Kubernetes (Deployment, Service, Ingress, ConfigMap, Secret, HPA)
  • Terraform (AWS, GCP, Azure infrastructure)
  • CI/CD (GitHub Actions, GitLab CI, CircleCI)
  • Web servers (Nginx, Apache configuration)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Configuration Generator AI skill do?

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.

Why use Configuration Generator on TypingMind?

Because you install it once and use it with any model. Configuration Generator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Configuration Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/configuration-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Configuration Generator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Configuration Generator?

As many as you like. As long as a model supports skills, you can use Configuration Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Configuration Generator AI skill free?

Yes. It is published on GitHub by ArabelaTso under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇