Agentic Jujutsu

This wraps Jujutsu version control with a self-learning layer that tracks AI agent operations and suggests approaches based on past successes. The ReasoningBank system records operation trajectories with success scores, then surfaces patterns when you face similar tasks later. Claims 87% auto-conflict resolution and 23x faster than Git for multi-agent work, though those numbers lack context. The quantum-resistant crypto (SHA3-512, HQC-128) feels like future-proofing overkill for most projects. Most interesting for teams running multiple AI agents that need to coordinate without stepping on each other, where the pattern learning could actually save debugging time once you've fed it enough trajectories.

skills.sh installs: 1.2K
GitHub stars: 81.4K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add ruvnet/ruflo --skill agentic-jujutsu --agent claude-code

Installs into .claude/skills of the current project.

Files

Agentic Jujutsu - AI Agent Version Control

Quantum-ready, self-learning version control designed for multiple AI agents working simultaneously without conflicts.

When to Use This Skill

Use agentic-jujutsu when you need:

✅ Multiple AI agents modifying code simultaneously
✅ Lock-free version control (23x faster than Git)
✅ Self-learning AI that improves from experience
✅ Quantum-resistant security for future-proof protection
✅ Automatic conflict resolution (87% success rate)
✅ Pattern recognition and intelligent suggestions
✅ Multi-agent coordination without blocking

Quick Start

Installation

npx agentic-jujutsu

Basic Usage

const { JjWrapper } = require('agentic-jujutsu');

const jj = new JjWrapper();

// Basic operations
await jj.status();
await jj.newCommit('Add feature');
await jj.log(10);

// Self-learning trajectory
const id = jj.startTrajectory('Implement authentication');
await jj.branchCreate('feature$auth');
await jj.newCommit('Add auth');
jj.addToTrajectory();
jj.finalizeTrajectory(0.9, 'Clean implementation');

SKILL.md on GitHub