AI-driven software development is enhanced by Code Foundations, a plugin designed to imbue AI code generation with senior engineering discipline. It addresses the technical debt often incurred by rapid, un-engineered LLM outputs by integrating proven checklists and quality gates directly into AI workflows. This tool is beneficial for engineers, researchers, and power users seeking to improve the reliability and maintainability of AI-generated code.

How It Works

The project employs a multi-stage workflow orchestrated by distinct commands: /code-foundations:research for clarifying intent through facilitated conversation, /code-foundations:plan for generating implementation-ready plans by auditing codebases and available skills, and /code-foundations:building for executing these plans with rigorous quality gates. A /code-foundations:debug command offers scientific debugging capabilities. This approach leverages subagent orchestration to ensure reliable skill loading and phase execution, automatically applying engineering best practices and mental models to AI coding tasks.

Quick Start & Requirements

Installation involves adding a marketplace and then installing the plugin:

• Add marketplace: /plugin marketplace add ryanthedev/rtd-claude-inn
• Install: /plugin install code-foundations@rtd
• Update: /plugin update code-foundations@rtd Specific hardware, OS, or library prerequisites are not detailed, suggesting it operates within its host AI environment.

Highlighted Details

• Integrated Workflow: Seamlessly guides AI from requirement clarification (research) through planning (plan) to gated execution (building) and debugging (debug).
• Quality Gates: The building phase enforces mandatory quality checks via subagents during BUILD and REVIEW phases, with adaptive policies (Full, Standard, Minimal) and per-phase commits for rollback.
• Scientific Debugging: The debug command implements a structured predict-log-run-resolve methodology, enhanced by task tracking to prevent context loss and rabbit holes.
• Subagent Orchestration: Actively fine-tuned for reliable skill loading and phase execution, saving artifacts for traceability.

Licensing & Compatibility

The project is licensed under the MIT license, which is permissive and generally compatible with commercial use and closed-source linking.

Limitations & Caveats

This plugin is experimental and under active development. Subagent orchestration is still being fine-tuned for stability, and official GitHub releases will be provided once the plugin stabilizes.