agentic-jujutsu

Files (2)

• LICENSE
• SKILL.md

SKILL.md (first lines)

# 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

```bash
npx agentic-jujutsu
```

### Basic Usage

```javascript
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:

```javascript
// 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) + '%');
```

**Validation (v2.3.1)**:
- ✅ Tasks must be non-empty (max 10KB)
- ✅ Success scores must be 0.0-1.0
- ✅ Must have operations before finalizing
- ✅ Contexts cannot be empty

### 2. Pattern Discovery

Automatically identify successful operation sequences:

```javascript
// Get discovered patterns
const patterns = JSON.parse(jj.getPatterns());

patterns.forEach(pattern => {
    console.log(`Pattern: ${pattern.name}`);
    console.log(`  Success rate: ${(pattern.successRate * 100).toFixed(1)}%`);
    console.log(`  Used ${pattern.observationCount} times`);
    console.log(`  Operations: ${pattern.operationSequence.join(' → ')}`);
    console.log(`  Confidence: ${(pattern.confidence * 100).toFixed(1)}%`);
});
```

### 3. Learning Statistics

Track improvement over time:

```javascript
const stats = JSON.parse(jj.getLearningStats());

console.log('Learning Progress:');
console.log(`  Total trajectories: ${stats.totalTrajectories}`);
console.log(`  Patterns discovered: ${stats.totalPatterns}`);
console.log(`  Average success: ${(stats.avgSuccessRate * 100).toFixed(1)}%`);
console.log(`  Improvement rate: ${(stats.improvementRate * 100).toFixed(1)}%`);
console.log(`  Prediction accuracy: ${(stats.predictionAccuracy * 100).toFixed(1)}%`);
```

### 4. Multi-Agent Coordination

Multiple agents work concurrently without conflicts:

```javascript
// Agent 1: Developer

From AlphaSaleAidan/opus-5-5-x-gpt-skill-tree, MIT.