
architecture-validation
by d-o-hub
A modular Rust-based self-learning episodic memory system for AI agents, featuring hybrid storage with Turso (SQL) and redb (KV), async execution tracking, reward scoring, reflection, and pattern-based skill evolution. Designed for real-world applicability, maintainability, and scalable agent workflows.
SKILL.md
name: architecture-validation description: 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.
Architecture Validation Skill
Dynamically validate that the implemented codebase matches architectural decisions documented in plan files.
Quick Reference
- Dimensions - What gets validated (components, dependencies, APIs, etc.)
- Workflow - Step-by-step validation process
- Extraction Patterns - How to extract architectural elements from plans
- Compliance - Compliance levels and report format
- Self-Learning - Continuous improvement framework
Purpose
Generic, adaptive framework that:
- Discovers all plan files in
plans/directory - Extracts architectural requirements dynamically
- Validates implementation compliance
- Reports gaps, drift, and violations
Key Principle: Be architecture-agnostic. Work with ANY project structure.
When to Use
- Validating implementation matches planning documents
- Checking for architecture drift after development
- Ensuring design decisions are followed
- Identifying missing implementations
- Preparing for architecture reviews
- Verifying refactoring didn't break boundaries
Validation Workflow
- Discover - Find all plan files
- Extract - Pull architectural elements
- Analyze - Compare vs codebase
- Validate - Check compliance
- Gap Analysis - Identify missing/drift/extra
- Report - Generate comprehensive report
See workflow.md for detailed phases and dimensions.md for validation categories.
Score
Total Score
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Reviews
Reviews coming soon



