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Container-native AI-enabled workspace implementing decoupled Client-Server Automation Architecture and MCP Gateway.

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Boomtick

Boomtick is a template repository providing foundational developer tooling, workflow orchestration, and an advanced AI review framework for autonomous agent systems. While framework-agnostic at its core, it includes built-in verification pipelines optimized for Vite applications (as demonstrated in arii/tech-dancer).

Boomtick strictly separates human-led strategic system design from agentic execution—reserving automation for repetitive engineering tasks, compilation, data validation, and operational compliance.


Quick Start

Initialize the agent environment and set up the local CLI:

./setup-agent.sh

Run the MCP server locally or register with your agent hosts:

# Register boomtick-mcp with local agent hosts (Antigravity CLI, Claude Code, Cursor, VS Code)
td mcp install --execute

# Or run directly via stdio
node mcp/dist/index.js

Repository Architecture

The codebase is split into two primary operational layers:

  • mcp/ (Model Context Protocol Server): Empowers AI agents with structured access to GitHub Pull Requests, repository state, CI logs, and validation tools. Facilitates conflict resolution and branch creation.
  • cli/ (td): A terminal-based fallback and local automation toolkit. Integrates directly with the GitHub CLI (gh) to handle manual PR audits, conflict detection, and runtime consistency checks.

Zero-Submodule Integration

Boomtick uses a direct standalone package, container, and composite action integration strategy. See docs/onboarding.md for how to integrate Boomtick into downstream repositories via PyPI (boomtick-cli), Docker (ghcr.io/arii/boomtick), npm (@arii/boomtick-mcp), or composite actions.


Advanced AI Integration

  • GitHub Models API: Low-latency code reviews and blast-radius analysis via OpenAI-compatible endpoints.
  • Gemini API: Multi-modal analysis for deep contextual and visual interface reviews.
  • Orchestration: Streamlined LLM routing and fallback strategies powered by @langchain/core.
  • Contextual Retrieval: RAG pipeline integration utilizing vector stores for automated triage and overlap analysis.

Planned Updates

  • Docker Container Packages & PyPI Packaging
  • Advanced RAG / Vector Store enhancements

Documentation Index


Showcase / Live Projects


Author

Created by Ariel Anders, creator of Boomtick Blog.


Tags: #DevAI #EngineeringOperations #GitHubModels #GeminiAPI #LangChain #RAG #VectorStore #AgenticExecution #MCP #TypeScript #Python #Vite

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