Traversable Skill Graph for Persistent AI Agent Memory in Codebases

A Practical Solution to Stateless AI Assistants

AI coding assistants lack persistent memory between sessions, forcing developers to start from zero each time. The common approach of dumping everything into large rules files like .cursorrules fails due to token limits and dilution of instructions.

The solution presented is progressive disclosure through a traversable skill graph that lives inside the codebase. The AI navigates this graph autonomously across sessions.

Three-Layer Architecture

The system has three distinct layers:

• Layer 1 (Always Loaded): Under 150 lines (300 tokens). Contains stack identity, folder conventions, and non-negotiables. Includes one outbound pointer to HANDOVER.md.
• Layer 2 (Loaded Per Session): HANDOVER.md serves as an attention router, not a document. It tells the AI which domain file to load based on current task (payments, auth, database, api-routes). Each domain file ends with instructions pointing to the next relevant file, creating a self-directing system.
• Layer 3 (Loaded Per Task): Prompt library with 12 categories. Each entry includes context, build, verify, and debug sections. The AI checks the index, loads the category, and follows the pattern.

Core Insight: Self-Directing Instructions

The key innovation is that instructions carry meaning, not just references. For example: "load security/threat-modeling.md before modifying webhook handlers" tells the AI when and why, not just what.

The developer has built this into a SaaS template that ships with the codebase, available at launchx.page for those wanting to examine the full graph structure.