agentic-jujutsu

Agentic Jujutsu - AI Agent Version Control

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
• Self-learning AI that improves from experience
• Pattern recognition and intelligent suggestions
• Multi-agent coordination without blocking

Note: this skill wraps the separate agentic-jujutsu npm package. Performance and feature claims should be verified against that package's own documentation before relying on them.

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');

// Get AI suggestions
const suggestion = JSON.parse(jj.getSuggestion('Add logout feature'));
console.log(`Confidence: ${suggestion.confidence}`);

Core Capabilities

1. Self-Learning with ReasoningBank

Track operations, learn patterns, and get intelligent suggestions:

// Start learning trajectory
const trajectoryId = jj.startTrajectory('Deploy to production');

// Perform operations (automatically tracked)
await jj.execute(['git', 'push', 'origin', 'main']);
await jj.branchCreate('release/v1.0');
await jj.newCommit('Release v1.0');

// Record operations to trajectory
jj.addToTrajectory();

// Finalize with success score (0.0-1.0) and critique
jj.finalizeTrajectory(0.95, 'Deployment successful, no issues');

// Later: Get AI-powered suggestions for similar tasks
const suggestion = JSON.parse(jj.getSuggestion('Deploy to staging'));
console.log('AI Recommendation:', suggestion.reasoning);
console.log('Confidence:', (suggestion.confidence * 100).toFixed(1) + '%');
console.log('Expected Success:', (suggestion.expectedSuccessRate * 100).toFixed(1) + '%');