parallel-agents

Name: parallel-agents
Availability: InStock
Author: adelpro

Coordinate multiple Claude Code agents for cross-domain analysis and unified synthesis reports.

34|29|Updated Jul 14, 2025

Search Tags:#automation #analysis #multi-agent #orchestration #parallel #agent-patterns

Ready to run

npx skills add https://github.com/adelpro/open-tarteel --skill parallel-agents-adelpro

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: parallel-agents
Source: https://github.com/adelpro/open-tarteel/tree/main/.agent/skills/parallel-agents
Command: npx skills add https://github.com/adelpro/open-tarteel --skill parallel-agents-adelpro

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates coordination of multiple specialized AI agents to tackle complex tasks requiring diverse domain expertise, delivering a unified analysis.

Core Features & Use Cases

Native Claude Code agent orchestration to run multiple domain experts in a single session.
Supports context passing, sequential and parallel execution patterns, and synthesis of findings into a single report.
Use cases include architecture reviews, security assessments, and end-to-end feature evaluations across frontend, backend, and data layers.

Quick Start

Instruct the orchestrator to map the project with explorer-agent, invoke the domain agents per Pattern 1, and synthesize the results.

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 cross-domain analysis in a single workflow?▼

Multi-agent orchestration coordinates specialized AI agents to perform cross-domain analysis within a single cohesive workflow, passing context between agents and synthesizing findings into a unified report. It supports sequential and parallel execution patterns for complex tasks requiring diverse domain expertise.

What is the best way to run a security assessment across frontend, backend, and QA domains?▼

Running a security assessment across frontend, backend, and QA domains is best handled by native Claude Code agent orchestration, which invokes multiple domain experts in a single session to evaluate end-to-end features and synthesize findings into one harmonized report.

How does context passing work between AI agents during parallel execution?▼

Context passing between AI agents during parallel execution works by sharing intermediate findings and domain-specific insights across specialized agents in a deterministic execution flow, ensuring each agent receives relevant context to perform its designated analysis task.

Can I use multi-agent orchestration for architecture reviews that require input from multiple domains?▼

Yes, multi-agent orchestration is specifically designed for architecture reviews requiring input from multiple domains. It maps the project with an explorer-agent, invokes domain agents per orchestration patterns, and synthesizes results into a unified report covering cross-domain requirements.

Do I need any external dependencies to run parallel AI agent patterns?▼

No external dependencies are required to run parallel AI agent patterns. The orchestration leverages built-in Claude Code agents with a deterministic execution flow, implementing context passing, resume support, and unified synthesis without additional components or libraries.

What are the limitations of using orchestration patterns for multi-agent analysis?▼

Orchestration patterns for multi-agent analysis are limited to tasks that benefit from cross-domain synthesis, such as architecture reviews and feature implementations. They follow a deterministic execution flow, which may not suit highly dynamic or unpredictable analysis scenarios requiring real-time agent adaptation.