Dynamically validate that the implemented codebase matches architectural decisions documented in plan files. Use when validating implementation matches planning documents, checking for architecture drift, or preparing for architecture reviews.
Quickly install architecture-validation AI skill to your development environment via command line
Source: d-o-hub/rust-self-learning-memory.
Dynamically validate that the implemented codebase matches architectural decisions documented in plan files.
Key Principle: Be architecture-agnostic. Work with ANY project structure.
See workflow.md for detailed phases and dimensions.md for validation categories.
Dynamically validate that the implemented codebase matches architectural decisions documented in plan files. Use when validating implementation matches planning documents, checking for architecture drift, or preparing for architecture reviews. Source: d-o-hub/rust-self-learning-memory.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/d-o-hub/rust-self-learning-memory --skill architecture-validationDynamically validate that the implemented codebase matches architectural decisions documented in plan files. Use when validating implementation matches planning documents, checking for architecture drift, or preparing for architecture reviews. Source: d-o-hub/rust-self-learning-memory.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/d-o-hub/rust-self-learning-memory --skill architecture-validation Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/d-o-hub/rust-self-learning-memory