k-kolomeitsev

k-kolomeitsev / data-structure-protocol

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

agent-skills ai calude claude-code code-generation
18
1
100% credibility
Found Feb 22, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Data Structure Protocol (DSP) is a lightweight, graph-based system for mapping codebase structure—including entities, dependencies, and reasons—to help AI coding agents navigate projects efficiently without full context loads.

How It Works

1
💡 Discover DSP for smarter coding

You learn about DSP, a simple map that helps AI assistants quickly understand your project's layout without getting lost.

2
📥 Add DSP to your project

Copy the ready-made helper files into your project's AI tools folder so it's all set up.

3
🗺️ Start your project map

Run one easy command to create a special folder where your project's map will live.

4
✨ Build the map piece by piece

As you work, add notes about your code files, key functions, and how they connect, noting what each does and why.

5
🤖 Share the map with your AI

Instruct your AI coding buddy to always check the map first to find its way around.

6
🔍 Explore and search with ease

Quickly search for code parts by name or meaning, and see connections and paths instantly.

🎉 AI works faster and better

Your AI now jumps right into tasks, remembers the structure, and makes changes reliably without wasting time.

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AI-Generated Review

What is data-structure-protocol?

Data Structure Protocol (DSP) builds a graph-based map of your codebase in a lightweight .dsp/ directory, tracking entities like modules and functions, their dependencies, public APIs, and reasons for every link. It's designed as long-term memory for LLM coding agents, letting them navigate structure via queries instead of dumping full repos into context windows. Built in Python with a CLI for init, create-object, add-import, search, and traversal commands, it works across languages for faster orientation and fewer tokens.

Why is it gaining traction?

Unlike static docs or AST parsers, DSP persists a queryable graph with "why" explanations, enabling graph-based RAG-like retrieval for code—think knowledge graph based RAG github but for dependencies and refactors. Agents using the included skill avoid the 5-15 minute "where is everything?" tax, spotting cycles, orphans, and impact paths instantly. Developers hook it into Cursor or Continue for reliable, token-efficient workflows on evolving projects.

Who should use this?

AI coding agent users on Cursor, Claude, or Continue tackling mid-sized Python or multi-lang repos, especially those doing frequent refactors or dependency hunts. Teams building graph-based recommendation systems github or long-term action recognition tools will like its protocol data structure for structural memory. Skip if your projects stay tiny or agents rarely lose context.

Verdict

Promising early experiment for graph-based LLM memory (10 stars, 1.0% credibility), with thorough docs and a robust CLI, but bootstrapping demands upfront effort on large codebases. Try it on a side project if agent context bloat frustrates you—solid foundation, just needs community miles.

(198 words)

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