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agentic-jujutsu

Coordinate multi-agent development workflows with self-learning trajectories and quantum-resistant security.

Updated Jul 2, 2025
Search Tags:#version control#AI agents#self-learning#trajectory#ReasoningBank#quantum-resistant#agentic-jujutsu
One-click install
npx skills add https://github.com/dug-21/neural-data-platform --skill agentic-jujutsu-dug-21
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-jujutsu
Source: https://github.com/dug-21/neural-data-platform/tree/main/.claude/skills/agentic-jujutsu
Command: npx skills add https://github.com/dug-21/neural-data-platform --skill agentic-jujutsu-dug-21

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the challenge of coordinating multiple AI agents in a development workflow without conflicts, enabling learning from outcomes and maintaining traceable trajectories across agents.

Core Features & Use Cases

  • Self-learning ReasoningBank trajectories to capture agent actions, decisions, and outcomes.
  • Automatic multi-agent coordination with conflict reduction and parallel execution.
  • Quantum-resistant security measures for trajectory integrity and secure communications.
  • Use Case: teams of AI agents collaborate on feature development, reviews, and deployment tasks with continuous learning.

Quick Start

Install and use the skill in your Node.js project:

  • Create a JjWrapper and start a trajectory: const jj = new JjWrapper(); jj.startTrajectory('Deploy to production');
  • Retrieve AI recommendations: const suggestion = JSON.parse(jj.getSuggestion('Deploy to production'));
  • Add to trajectory and finalize: jj.addToTrajectory(); jj.finalizeTrajectory(0.9, 'Deployment successful');

Frequently Asked Questions about agentic-jujutsu

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I coordinate multiple AI agents in a version control workflow without conflicts?▼

Multi-agent AI version control coordination is automated through self-learning trajectories and automatic conflict resolution, enabling agents to operate concurrently on features and deployments without collisions.

How do I capture AI agent decisions and outcomes for continuous learning?▼

You capture agent decisions and outcomes by using ReasoningBank trajectories, which record actions, decisions, and results to generate AI-driven suggestions and learning statistics for future operations.

Does this multi-agent version control skill require any specific dependencies or frameworks?▼

No specific dependencies are required to run this skill. It operates independently within a Node.js environment, allowing you to instantiate a wrapper and start managing agent trajectories directly.

How do I start tracking a deployment trajectory and retrieve AI suggestions?▼

You start tracking by creating a JjWrapper and calling startTrajectory, then retrieve AI suggestions by parsing the output of getSuggestion, adding results to the trajectory, and finalizing it.

How are agent communications and trajectory integrity secured against future threats?▼

Agent communications and trajectory integrity are secured using quantum-resistant primitives, ensuring that operation tracking and trajectory management remain protected against emerging computational threats.

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