parallel-agents

Coordinate multiple AI agents to analyze a codebase across security, architecture, and testing domains.

Updated Jan 21, 2026

Search Tags:#workflow #ai #multi-agent #agents #orchestration #synthesis

Ready to run

npx skills add https://github.com/ollieb89/orchestrator --skill parallel-agents-ollieb89

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: parallel-agents
Source: https://github.com/ollieb89/orchestrator/tree/main/.cursor/skills/parallel-agents
Command: npx skills add https://github.com/ollieb89/orchestrator --skill parallel-agents-ollieb89

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinating multiple specialized AI agents to tackle complex tasks that require diverse domain expertise within a single orchestration workflow.

Core Features & Use Cases

Multi-agent orchestration across domains, enabling parallel or sequential agent chains.
Context passing and synthesis to generate a unified plan from diverse findings.
Resume and reuse support to continue previous agent work and adapt to new tasks.

Quick Start

Initiate a parallel-agent orchestration across domains to analyze a codebase.

Frequently Asked Questions about parallel-agents

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

FAQPage Schema

How do I coordinate multiple AI agents for codebase analysis?▼

Coordinating multiple AI agents for codebase analysis involves orchestrating specialized agents in parallel or sequential chains to evaluate security, architecture, and testing perspectives across large repositories. It passes context between agents to synthesize a unified plan from diverse findings.

What is multi-agent orchestration in software engineering?▼

Multi-agent orchestration is the coordination of specialized AI agents to tackle complex tasks requiring diverse domain expertise within a single workflow. It enables sequential or parallel agent chains and synthesizes diverse findings into a unified, actionable plan.

Can I use parallel agents to analyze large repositories for security and architecture issues?▼

Yes, you can use multi-agent orchestration to analyze large repositories for security and architecture issues. The approach applies multi-domain analysis across large codebases, ensuring agent coordination and context passing to generate a synthesized, unified plan.

How do I synthesize findings from separate AI agents into a unified plan?▼

To synthesize findings from separate AI agents into a unified plan, you use context passing within the orchestration workflow. This approach aggregates diverse domain perspectives and generates a synthesized, unified plan across the codebase analysis.

Can I resume previous AI agent work if my multi-domain analysis is interrupted?▼

Yes, you can resume previous AI agent work if your multi-domain analysis is interrupted. The orchestration workflow supports resume and reuse capabilities, allowing you to continue previous agent work and adapt to new tasks.

What are the limitations of using a multi-agent workflow for repository analysis?▼

The main limitation of using a multi-agent workflow for repository analysis is the complexity of context passing and synthesis. It is best suited for large repositories requiring diverse domain expertise rather than simple, single-domain tasks.