Agentic Dev Boilerplate

A comprehensive template system for generating agentic development workflows and automation infrastructure.

Version: 1.1.1
Repository: https://github.com/tzervas/agentic-dev-boilerplate

Overview

This project provides a complete foundation for building agentic development systems with automated multi-agent coordination, intelligent task tracking, and comprehensive CI/CD pipelines. It includes specialized agents for planning, testing, debugging, deployment, and system engineering, all working together to streamline development workflows.

Quick Start

Installation

From PyPI (Recommended)

pip install agentic-dev-boilerplate

From Source

git clone https://github.com/tzervas/agentic-dev-boilerplate
cd agentic-dev-boilerplate
pip install -e .

Generate Boilerplate

Create a new project with the CLI:

# Generate boilerplate for your project
agentic-dev-boilerplate --schema project-schema.yaml --output ./my-project

# Or use short options
agentic-dev-boilerplate -s my-schema.yaml -o ./my-project

Prerequisites

• Python 3.11 or later
• Git 2.30+ (for version control)
• GPG (for commit signing)

Development Setup

1. Install UV (fast Python package manager)

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Clone the repository

   git clone https://github.com/tzervas/agentic-dev-boilerplate
   cd agentic-dev-boilerplate
   

3. Set up development environment

   uv venv
   uv pip install -e .
   

4. Configure Git

   python scripts/git_setup.py --setup
   

Testing

Comprehensive Test Suite

Run the complete validation suite:

./test-package.sh

Docker Testing

For isolated testing and validation:

# Build and run tests
docker-compose up --build

# Or build and test manually
docker build -t agentic-boilerplate-test .
docker run --rm agentic-boilerplate-test

Development Workflow

1. Task Planning

Use the planner agent to break down features and create implementation roadmaps. Tasks are tracked in tasking/tracker.yaml with context files stored in tasking/context/.

2. Implementation

• Create feature branches: git checkout -b feature/task-name
• Implement changes following project standards
• Write comprehensive tests for new functionality

3. Testing & Validation

# Run validation suite
python scripts/validation_scripts.py

# Run tests
uv run pytest

4. Create Pull Request

# Create PR with automation
python scripts/create_pr_local.py

# Or use GitHub CLI
gh pr create --fill

5. Code Review

Pull requests are automatically enriched with labels and milestones. Validation runs automatically on PR events to ensure code quality and standards compliance.

Multi-Agent Coordination

The system supports collaborative problem-solving through coordinated multi-agent workflows.

Starting Multi-Agent Sessions

# Solve complex problems with multiple agents
python scripts/multi_agent_solver.py \
  --problem "Implement user authentication system" \
  --agents planner tester debugger deployer \
  --consensus-threshold 0.8

Agent Coordination Features

• Orchestrated Problem Decomposition: Complex tasks are automatically broken down into manageable components
• Cross-Agent Validation: Solutions are validated by multiple specialized agents
• Consensus Building: Team agreement on optimal approaches and solutions
• Collaborative Tracking: Shared progress monitoring and status updates
• Coordinated Execution: Synchronized implementation across all participating agents

Available Agents

• Planner: Task decomposition, roadmap generation, and agent routing
• Tester: Validation suites, test execution, and result analysis
• Debugger: Root cause analysis, log processing, and patch development
• Deployer: Production deployment, rollback orchestration, and change management
• Systems Engineer: Hardware emulation, IOMMU/VFIO configuration, and GPU passthrough
• DevOps Specialist: Infrastructure automation, CI/CD pipelines, and network orchestration
• Orchestrator: Multi-agent coordination, collaborative problem-solving, and team consensus
• Software Engineer: Code implementation, refactoring, and architecture design
• AI Engineer: ML model development, AI integration, and data pipeline optimization

GitHub Integration

Label issues or pull requests to trigger multi-agent coordination:

• testing → Involves tester agent
• debug → Involves debugger agent
• deploy → Involves deployer agent
• infra → Involves systems engineer and DevOps specialist

Project Structure

├── .github/
│   ├── instructions/          # Agent instruction files
│   ├── prompts/              # Reusable prompt templates
│   ├── scripts/              # PR automation scripts
│   └── workflows/            # GitHub Actions CI/CD
├── scripts/                  # Utility scripts
├── tasking/                  # Task tracking system
│   ├── tracker.yaml         # Main task tracker
│   ├── context/             # Task context files
│   └── plan.md              # Project roadmap
├── docs/                    # Documentation
├── src/                     # Source code
├── tests/                   # Test files
├── requirements.txt         # Python dependencies
└── README.md

Agent System

Core Agents

• Planner: Task decomposition, roadmap generation, and agent routing
• Tester: Validation suites, test execution, and result analysis
• Debugger: Root cause analysis, log processing, and patch development
• Deployer: Production deployment, rollback orchestration, and change management
• Systems Engineer: Hardware emulation, IOMMU/VFIO configuration, and GPU passthrough
• DevOps Specialist: Infrastructure automation, CI/CD pipelines, and network orchestration
• Orchestrator: Multi-agent coordination, collaborative problem-solving, and team consensus
• Software Engineer: Code implementation, refactoring, and architecture design
• AI Engineer: ML model development, AI integration, and data pipeline optimization

Agent Instructions

Each agent has specialized instructions in .github/instructions/ that define their role, responsibilities, workflow integration patterns, and success metrics.

Quality Assurance

Automated Validation

• PR Automation: Automatic labeling, milestone assignment, and issue linking
• Code Quality: Linting, formatting, and type checking
• Testing: Unit, integration, and system test coverage
• Security: Dependency scanning and vulnerability assessment

Manual Reviews

• Code review requirements and standards
• Architecture decision documentation
• Performance and scalability considerations
• Security impact analysis

Contributing

1. Follow the workflow: Use task tracking and agent coordination for all changes
2. Write tests: Ensure comprehensive test coverage for new functionality
3. Sign commits: All commits must be GPG signed for verification
4. Create pull requests: Use the automated PR creation tools

Commit Standards

• Use conventional commit format: type(scope): description
• Sign all commits with GPG: git commit -S
• Reference task IDs in commit messages when applicable

Pull Request Requirements

• All automated validation checks must pass
• Appropriate test coverage maintained
• Documentation updated for any user-facing changes
• Task tracker updated with completion status

CI/CD Pipeline

Pipeline Stages

• Validate: Code linting, testing, and security scanning
• Build: Package building and artifact creation
• Deploy: Staging deployment and production releases

Quality Gates

• Code linting and formatting checks
• Test execution with coverage requirements
• Security vulnerability scanning
• Minimum 80% code coverage threshold

Security

• Commit Signing: All commits must be GPG signed for authenticity
• Dependencies: Regular security scanning and dependency updates
• Secrets: Automated detection and prevention of exposed secrets

Documentation

• API Reference: Comprehensive API documentation
• User Guides: Setup and usage instructions
• Contributing Guide: Development workflow and standards

Support

• Issues: Use GitHub issues with appropriate labels for bug reports and feature requests
• Discussions: Technical discussions and community Q&A
• Documentation: Comprehensive guides available in the /docs directory

License

See LICENSE file for details.