jin-orchestrator

Orchestrate multi-agent workflows with task decomposition, dependency-based execution, and automated verification.

2|Updated Mar 3, 2026
One-click install
npx skills add https://github.com/cjinzy/jin-claude --skill jin-orchestrator
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: jin-orchestrator
Source: https://github.com/cjinzy/jin-claude/tree/main/plugins/jin-claude/skills/jin-orchestrator
Command: npx skills add https://github.com/cjinzy/jin-claude --skill jin-orchestrator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the complex process of breaking down large tasks into smaller, manageable steps, assigning them to the right AI agents, executing them, and verifying the results, ensuring high-quality outcomes with minimal manual intervention.

Core Features & Use Cases

  • Task Decomposition: Breaks down complex requests into atomic tasks with defined dependencies.
  • Intelligent Agent Assignment: Selects the most suitable agent (e.g., SWE Agent, Python Expert, specialized Opus agents) for each task based on complexity and domain.
  • Parallel & Sequential Execution: Manages task execution respecting dependencies, allowing for parallel processing where possible.
  • Automated Verification & Fix Loop: Independently verifies completed tasks and initiates an automated correction loop for up to three retries if issues are found.
  • Use Case: When faced with a large feature request that involves modifying multiple files and requires deep code understanding, this Skill will plan, execute, and verify the entire implementation, ensuring it meets all requirements and passes verification checks.

Quick Start

Use the jin-orchestrator skill to orchestrate the implementation of a new user authentication module.

Frequently Asked Questions about jin-orchestrator

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

FAQPage Schema
How do I automate multi-agent workflows for complex software development tasks?

To automate multi-agent workflows, you decompose complex software development tasks into atomic steps, assign them to specialized agents, and execute them based on dependency graphs. This manages parallel and sequential processing while performing independent automated verification.

How does task decomposition work for multi-agent code generation and verification?

Task decomposition for multi-agent code generation breaks large requests into smaller, manageable atomic tasks with defined dependencies. It allows intelligent assignment to specialized agents and manages parallel execution where dependencies allow.

Can I use specialized agents like SWE Agent and Python Expert for specific coding tasks?

Yes, you can use specialized agents like SWE Agent or Python Expert for specific coding tasks. Intelligent agent assignment selects the most suitable agent for each task based on complexity and domain, ensuring appropriate execution.

What is the best way to verify multi-agent code implementation and fix errors automatically?

The best way to verify multi-agent code implementation is using an independent verifier to check completed tasks. If issues are found, an automated correction loop initiates up to three retries to fix the errors automatically.

Are there limitations to the automated fix loop during multi-agent task orchestration?

Yes, a limitation of the automated fix loop during multi-agent task orchestration is that it initiates an automated correction loop for a maximum of three retries if verification issues are found, then stops.