🚀 pipeline-generator

Generate production-ready CI/CD pipeline configs from a simple YAML spec.

One spec file → pipeline configs for GitHub Actions, Azure DevOps, and GitLab CI. Stop writing boilerplate YAML manually.

pipe-gen generate --demo

✨ Features

Feature	Description
🐙 GitHub Actions	Generates .github/workflows/ci.yml with matrix testing, caching, Docker builds
🔷 Azure DevOps	Generates azure-pipelines.yml with stages, test publishing, deployment jobs
🦊 GitLab CI	Generates .gitlab-ci.yml with parallel matrix, DinD builds, environments
🔍 Auto-Detect	Scans your project to detect language, framework, and existing CI configs
📝 Simple Spec	Define your pipeline in a human-readable YAML spec file
🎨 Rich Output	Beautiful terminal output with previews, syntax highlighting, and summaries
🏗️ 5 Languages	Python, Node.js, Go, .NET, Terraform — with smart defaults for each
🔒 Security	Built-in security scanning stages (Bandit, Safety, npm audit, gosec, tfsec)
🐳 Docker	Docker build & push with metadata extraction and multi-platform support
🚀 Deploy	Environment-based deployments with manual approval gates

📦 Installation

pip install pipeline-generator

Or install from source:

git clone https://github.com/SanjaySundarMurthy/pipeline-generator.git
cd pipeline-generator
pip install -e ".[dev]"

🚀 Quick Start

1. Create a spec file

# From a preset
pipe-gen init --preset python --name my-api

# Or auto-detect your project
pipe-gen detect

2. Edit the spec (optional)

# pipeline.yaml
project:
  name: my-api
  language: python
  version: "3.11"
  framework: fastapi

stages:
  - lint
  - test
  - security
  - build
  - deploy

lint:
  tools:
    - ruff

test:
  framework: pytest
  coverage: true
  min_coverage: 80

security:
  tools:
    - bandit
    - safety

build:
  type: docker
  dockerfile: Dockerfile
  registry: ghcr.io

deploy:
  target: kubernetes
  environments:
    - name: staging
      auto_deploy: true
    - name: production
      auto_deploy: false

3. Generate pipeline configs

# Generate for all platforms
pipe-gen generate --platform all

# Generate for a specific platform
pipe-gen generate --platform github

# Preview without writing files
pipe-gen generate --dry-run

# Try the demo (no files needed)
pipe-gen generate --demo

📋 CLI Reference

Command	Description
pipe-gen init	Create a pipeline.yaml spec file
pipe-gen detect	Auto-detect project type
pipe-gen generate	Generate CI/CD pipeline configs
pipe-gen --version	Show version

pipe-gen init

Options:
  -p, --preset   Language preset (python, node, go, dotnet, terraform)
  -n, --name     Project name
  -o, --output   Output file path (default: pipeline.yaml)

pipe-gen generate

Options:
  -p, --platform   Target platform: all, github, azure, gitlab (default: all)
  -s, --spec       Path to spec file (default: pipeline.yaml)
  --preset         Use a built-in preset instead of spec file
  -o, --output-dir Output directory (default: current dir)
  --dry-run        Preview without writing files
  --demo           Demo mode with sample output

🗣️ Supported Languages

Language	Lint Tools	Test Framework	Security Tools	Setup Action
Python	ruff, mypy, flake8, black	pytest, unittest	bandit, safety	setup-python@v5
Node.js	eslint, prettier	jest, vitest, mocha	npm audit	setup-node@v4
Go	golangci-lint, go vet	go test	gosec, govulncheck	setup-go@v5
.NET	dotnet format	dotnet test	dotnet audit	setup-dotnet@v4
Terraform	terraform fmt, tflint	terraform validate	tfsec, checkov	setup-terraform@v3

🏗️ Generated Pipeline Structure

Each generated pipeline includes (based on your spec):

lint  →  test (matrix)  →  security  →  build (Docker)  →  deploy
                                                          ├── staging (auto)
                                                          └── production (manual)

What's included:

• Lint: Code style checking, formatting validation
• Test: Matrix testing across language versions, coverage reports
• Security: SAST scanning, dependency vulnerability checks
• Build: Docker build & push with image metadata and tagging
• Deploy: Environment-based deployments with approval gates

🔧 Advanced Usage

Use a preset without a spec file

pipe-gen generate --preset python --platform github --dry-run

Generate in a different directory

pipe-gen generate --output-dir ./ci-configs

Auto-detect and generate

pipe-gen detect           # See what's detected
pipe-gen init             # Create spec from detection
pipe-gen generate         # Generate configs

🧪 Running Tests

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest -v

# Run with coverage
pytest --cov=pipeline_generator -v

📁 Project Structure

pipeline-generator/
├── pipeline_generator/
│   ├── __init__.py          # Package version
│   ├── cli.py               # Click CLI commands
│   ├── models.py            # PipelineSpec data models
│   ├── presets.py            # Language configs & presets
│   ├── detector.py           # Project auto-detection
│   ├── generator.py          # Generation engine
│   ├── platforms/
│   │   ├── base.py          # Abstract base class
│   │   ├── github.py        # GitHub Actions generator
│   │   ├── azure.py         # Azure DevOps generator
│   │   └── gitlab.py        # GitLab CI generator
│   └── output/
│       └── console.py       # Rich terminal output
├── tests/
│   ├── conftest.py          # Test fixtures
│   └── test_generator.py    # 35+ tests
├── examples/                # Example spec files
├── pyproject.toml
├── README.md
└── CONTRIBUTING.md

🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md for details.

Adding a new language

1. Add tool config to presets.py → LANGUAGE_TOOLS
2. Add preset to PRESETS
3. Add detection in detector.py
4. Add tests and an example spec

Adding a new platform

1. Create generator in platforms/
2. Inherit from BasePlatform
3. Register in generator.py → PLATFORMS

📄 License

MIT License — see LICENSE for details.

👤 Author

Sanjay S — Senior DevOps Engineer

• GitHub: @SanjaySundarMurthy
• LinkedIn: sanjaysundarmurthy
• Portfolio: sanjaysundarmurthy-portfolio.vercel.app