PipelineAI

A CLI tool that generates GitHub Actions, GitLab CI, and Dockerfile configurations from a simple YAML spec. Inspired by DevOps AI automation trends.

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Architecture

graph TD
    A[User YAML Spec] -->|pipelineai generate| B[PipelineAI Core]
    B --> C{Project Detection}
    C -->|Python| D[Python Templates]
    C -->|Node.js| E[Node Templates]
    C -->|Rust| F[Rust Templates]
    C -->|Go| G[Go Templates]
    D --> H[GitHub Actions]
    D --> I[GitLab CI]
    D --> J[Dockerfile]
    E --> H
    E --> I
    E --> J
    F --> H
    F --> I
    F --> J
    G --> H
    G --> I
    G --> J
    H --> K[Output Files]
    I --> K
    J --> K

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Quickstart

Installation

pip install -e .

Usage

Auto-detect project and generate pipelines:

# Detect project type and generate GitHub Actions workflow
pipelineai generate --target github --path .

# Generate GitLab CI config
pipelineai generate --target gitlab --path .

# Generate a Dockerfile
pipelineai generate --target dockerfile --path .

# Generate all configs at once
pipelineai generate --target all --path .

Use a YAML spec file:

# Generate from a spec file
pipelineai generate --spec pipeline.yaml --output ./ci-output

Detect project type:

pipelineai detect --path .

Example Spec (pipeline.yaml)

name: my-app
language: python
version: "3.11"
services:
  - postgres
  - redis
steps:
  - name: lint
    command: "ruff check ."
  - name: test
    command: "pytest tests/"
  - name: build
    command: "python -m build"

Features

• Auto-detect Python, Node.js, Rust, and Go projects
• Generate GitHub Actions workflows
• Generate GitLab CI/CD pipelines
• Generate optimized Dockerfiles
• Extensible step and service system
• Pipeline optimization (caching, parallelism)
• Simple YAML spec format

Development

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

# Run tests
make test

# Lint
make lint

# Format
make format

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