
template-repo
Template repo using AI agents and custom MCP tooling
Stars: 74

The template-repo is a comprehensive development ecosystem with 6 AI agents, 14 MCP servers, and complete CI/CD automation running on self-hosted, zero-cost infrastructure. It follows a container-first approach, with all tools and operations running in Docker containers, zero external dependencies, self-hosted infrastructure, single maintainer design, and modular MCP architecture. The repo provides AI agents for development and automation, features 14 MCP servers for various tasks, and includes security measures, safety training, and sleeper detection system. It offers features like video editing, terrain generation, 3D content creation, AI consultation, image generation, and more, with a focus on maximum portability and consistency.
README:
A comprehensive development ecosystem with 6 AI agents, 14 MCP servers, and complete CI/CD automation - all running on self-hosted, zero-cost infrastructure.
This project follows a container-first approach:
- All tools and CI/CD operations run in Docker containers for maximum portability
- Zero external dependencies - runs on any Linux system with Docker
- Self-hosted infrastructure - no cloud costs, full control over runners
- Single maintainer design - optimized for individual developer productivity
- Modular MCP architecture - Separate specialized servers for different functionalities
Six AI agents working in harmony for development and automation. See AI Agents Documentation for complete details:
- Claude Code - Primary development assistant
- Codex - AI-powered code generation and completion (Setup Guide)
- OpenCode - Comprehensive code generation (Integration Guide)
- Crush - Fast code generation (Quick Reference)
- Gemini CLI - Interactive development & automated PR reviews
- GitHub Copilot - Code review suggestions
Security: Keyword triggers, user allow list, secure token management. See AI Agents Security
Safety Training: Essential AI safety concepts for human-AI collaboration. See Human Training Guide
Sleeper Detection: Advanced AI agent backdoor and sleeper detection system. See Sleeper Detection Package
- 14 MCP Servers - Modular tools for code quality, content creation, AI assistance, 3D graphics, video editing, speech synthesis, virtual characters, and more
- 6 AI Agents - Comprehensive development automation
- Sleeper Detection System - Advanced AI backdoor detection using TransformerLens residual stream analysis
- Company Integration - Custom agent builds for corporate AI APIs (Documentation)
- Video Editor - AI-powered video editing with transcription, speaker diarization, and intelligent scene detection
- Gaea2 Terrain Generation - Professional terrain generation
- Blender 3D Creation - Full 3D content creation, rendering, and simulation
- ComfyUI & AI Toolkit - Image generation and LoRA training
- Container-First Architecture - Maximum portability and consistency
- Self-Hosted CI/CD - Zero-cost GitHub Actions infrastructure
- Automated PR Workflows - AI-powered reviews and fixes
New to the template? Check out our Template Quickstart Guide for step-by-step customization instructions!
- Choose from 4 pre-configured paths (Minimal, AI-Powered, Content Creation, Full Stack)
- Learn what to enable/disable for your specific use case
- Get up and running in 15 minutes
-
Prerequisites
- Linux system (Ubuntu/Debian recommended)
- Docker (v20.10+) and Docker Compose (v2.0+)
- No other dependencies required!
-
Clone and setup
git clone https://github.com/AndrewAltimit/template-repo cd template-repo # Install AI agents package (for CLI tools) pip3 install -e ./packages/github_ai_agents # Set up API keys (if using AI features) export OPENROUTER_API_KEY="your-key-here" # For OpenCode/Crush export GEMINI_API_KEY="your-key-here" # For Gemini (dont use API key for free tier, use web auth) # For Codex: run 'codex auth' after installing @openai/codex
-
Use MCP servers with Claude Code and other agents
- MCP servers are configured in
.mcp.json
- No manual startup required! Agents can start the services themselves.
- MCP servers are configured in
-
For standalone usage
# Start HTTP servers for testing/development docker-compose up -d # Test all servers python automation/testing/test_all_servers.py --quick # Use AI agents directly ./tools/cli/agents/run_codex.sh # Interactive Codex session ./tools/cli/agents/run_opencode.sh -q "Create a REST API" ./tools/cli/agents/run_crush.sh -q "Binary search function" ./tools/cli/agents/run_gemini.sh # Interactive Gemini CLI session
For detailed setup instructions, see CLAUDE.md
For enterprise environments that require custom certificates (e.g., corporate proxy certificates, self-signed certificates), we provide a standardized installation mechanism:
-
Certificate Installation Script:
automation/corporate-proxy/shared/scripts/install-corporate-certs.sh
- Placeholder script that organizations can customize with their certificate installation process
- Automatically executed during Docker image builds for all corporate proxy containers
-
Customization Guide: See
automation/corporate-proxy/shared/scripts/README.md
for:- Step-by-step instructions on customizing the certificate installation
- Examples for different certificate formats and installation methods
- Best practices for certificate management in containerized environments
-
Affected Services:
- All corporate proxy integrations (Gemini, Codex, OpenCode, Crush)
- Python CI/CD containers
- Any custom Docker images built from this template
This pattern ensures consistent certificate handling across all services while maintaining security and flexibility for different corporate environments.
.
├── .github/workflows/ # GitHub Actions workflows
├── docker/ # Docker configurations
├── packages/ # Installable packages
│ ├── github_ai_agents/ # AI agent implementations
│ └── sleeper_detection/ # AI backdoor detection system
├── tools/ # MCP servers and utilities
│ ├── mcp/ # Modular MCP servers
│ │ ├── code_quality/ # Formatting & linting
│ │ ├── content_creation/ # Manim & LaTeX
│ │ ├── gemini/ # AI consultation
│ │ ├── gaea2/ # Terrain generation
│ │ ├── blender/ # 3D content creation
│ │ ├── codex/ # AI-powered code generation
│ │ ├── opencode/ # Code generation
│ │ ├── crush/ # Code generation
│ │ ├── video_editor/ # AI-powered video editing
│ │ ├── meme_generator/ # Meme creation
│ │ ├── elevenlabs_speech/# Speech synthesis
│ │ ├── virtual_character/# AI agent embodiment
│ │ ├── ai_toolkit/ # LoRA training interface
│ │ ├── comfyui/ # Image generation interface
│ │ └── core/ # Shared components
│ └── cli/ # Command-line tools
│ ├── agents/ # Agent runner scripts
│ │ ├── run_claude.sh # Claude Code Runner
│ │ ├── run_codex.sh # Codex runner
│ │ ├── run_opencode.sh # OpenCode runner
│ │ ├── run_crush.sh # Crush runner
│ │ └── run_gemini.sh # Gemini CLI runner
│ ├── containers/ # Container runner scripts
│ │ ├── run_codex_container.sh # Codex in container
│ │ ├── run_opencode_container.sh # OpenCode in container
│ │ ├── run_crush_container.sh # Crush in container
│ │ └── run_gemini_container.sh # Gemini in container
│ └── utilities/ # Other CLI utilities
├── automation/ # CI/CD and automation scripts
│ ├── analysis/ # Code and project analysis tools
│ ├── ci-cd/ # CI/CD pipeline scripts
│ ├── corporate-proxy/ # Corporate proxy integrations
│ │ ├── gemini/ # Gemini CLI proxy wrapper
│ │ ├── codex/ # Codex proxy wrapper
│ │ ├── opencode/ # OpenCode proxy wrapper
│ │ ├── crush/ # Crush proxy wrapper
│ │ └── shared/ # Shared proxy components
│ ├── launchers/ # Service launcher scripts
│ ├── monitoring/ # Service and PR monitoring
│ ├── review/ # Code review automation
│ ├── scripts/ # Utility scripts
│ ├── security/ # Security and validation
│ ├── sleeper-detection/ # Sleeper detection automation
│ ├── setup/ # Setup and installation scripts
│ └── testing/ # Testing utilities
├── tests/ # Test files
├── docs/ # Documentation
├── config/ # Configuration files
- Code Quality - Formatting, linting, auto-formatting
- Content Creation - Manim animations, LaTeX, TikZ diagrams
- Gaea2 - Terrain generation (Documentation)
- Blender - 3D content creation, rendering, physics simulation (Documentation)
- Gemini - AI consultation (containerized and host modes available)
- Codex - AI-powered code generation and completion
- OpenCode - Comprehensive code generation
- Crush - Fast code snippets
- Meme Generator - Create memes with templates
- ElevenLabs Speech - Advanced TTS with v3 model, 50+ audio tags, 74 languages (Documentation)
- Video Editor - AI-powered video editing with transcription and scene detection (Documentation)
- Virtual Character - AI agent embodiment in virtual worlds (VRChat, Blender, Unity) (Documentation)
- AI Toolkit - LoRA training interface (remote: 192.168.0.152:8012)
- ComfyUI - Image generation interface (remote: 192.168.0.152:8013)
-
STDIO Mode (local MCPs): Configured in
.mcp.json
, auto-started by Claude - HTTP Mode (remote MCPs): Run the MCP using docker-compose on the remote node.
See MCP Architecture Documentation and STDIO vs HTTP Modes for details.
For complete tool listings, see MCP Tools Reference
See .env.example
for all available options.
-
.mcp.json
- MCP server configuration for Claude Code -
docker-compose.yml
- Container services configuration -
CLAUDE.md
- Project-specific Claude Code instructions (root directory) -
CRUSH.md
- Crush AI assistant instructions (root directory) -
AGENTS.md
- Universal AI agent configuration and guidelines (root directory) -
docs/ai-agents/project-context.md
- Context for AI reviewers
All Python operations run in Docker containers:
# Run CI operations
./automation/ci-cd/run-ci.sh format # Check formatting
./automation/ci-cd/run-ci.sh lint-basic # Basic linting
./automation/ci-cd/run-ci.sh test # Run tests
./automation/ci-cd/run-ci.sh full # Full CI pipeline
# Run specific tests
docker-compose run --rm python-ci pytest tests/test_mcp_tools.py -v
- Pull Request Validation - Automatic Gemini AI review
- Continuous Integration - Full CI pipeline
- Code Quality - Multi-stage linting (containerized)
- Automated Testing - Unit and integration tests
- Security Scanning - Bandit and safety checks
All workflows run on self-hosted runners for zero-cost operation.
- AGENTS.md - Universal AI agent configuration and guidelines
- CLAUDE.md - Claude-specific instructions and commands
- CRUSH.md - Crush AI assistant instructions
- MCP Architecture - Modular server design
- AI Agents Documentation - Seven AI agents overview
- Template Quickstart Guide - Customize the template for your needs
- Self-Hosted Runner Setup
- GitHub Environments Setup
- Containerized CI
This project is released under the Unlicense (public domain dedication).
For jurisdictions that do not recognize public domain: As a fallback, this project is also available under the MIT License.
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