Graph-based long-term memory skill for AI (LLM) coding agents — faster context, fewer tokens, safer refactors

Data Structure Protocol (DSP)

The missing memory layer for AI-assisted development

The problem

Your agent re-reads the same codebase every session. DSP fixes that.

Every time you start a new task, your AI coding agent spends the first 5–15 minutes "getting oriented" — scanning files, tracing imports, figuring out what depends on what. On large projects this becomes a constant tax on tokens and attention. Context is rebuilt from scratch, every single time.

DSP is a graph-based long-term structural memory stored in .dsp/. It gives agents a persistent, versionable map of your codebase — entities, dependencies, public APIs, and the reasons behind every connection — so they can pick up exactly where they left off.

DSP is not another workflow framework. It's the persistent structural memory layer that's missing from every AI coding workflow.

Install

macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.sh | bash

Windows:

irm https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.ps1 | iex

Codex:

$skill-installer install https://github.com/k-kolomeitsev/data-structure-protocol/tree/main/skills/data-structure-protocol

What you get

• Agent stops re-learning your project every session — structural context persists across tasks, sessions,