team-builder

Discover, select, and run multiple AI agents in parallel to synthesize unified reports.

2|Updated Mar 3, 2026

Search Tags:#workflow #orchestration #parallel #ai-agent #team #domain-discovery

Ready to run

npx skills add https://github.com/MomoDaviluke/star-citizen-promotion --skill team-builder-momodaviluke

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: team-builder
Source: https://github.com/MomoDaviluke/star-citizen-promotion/tree/main/.agents/skills/ecc/team-builder
Command: npx skills add https://github.com/MomoDaviluke/star-citizen-promotion --skill team-builder-momodaviluke

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinating multiple AI agents in parallel to form task-focused teams, enabling rapid, hands-off collaboration across domains.

Core Features & Use Cases

Dynamic discovery of agent prompts across a repository (flat and subdirectory layouts)
Domain-based selection, parallel spawning, and autonomous task execution
Parallel synthesis of agent outputs into a unified report with highlights of agreements and tensions
Use Case: quickly assemble an engineering and marketing team to audit a project and synthesize recommendations

Quick Start

Select domains or agents, provide a task description, and run the parallel agent team immediately.

Frequently Asked Questions about team-builder

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

FAQPage Schema

How do I coordinate multiple AI agents in parallel to form task-focused teams?▼

To coordinate multiple AI agents in parallel, you provide a task description and select domains, which triggers dynamic discovery and parallel spawning of expert agent teams for autonomous execution and unified synthesis.

Can I dynamically discover AI agent prompts across different repository layouts?▼

Yes, dynamic discovery of AI agent prompts is supported across both flat and subdirectory repository layouts, enabling non-hardcoded selection of domain-specific agent personas before parallel execution begins.

How does parallel AI agent orchestration synthesize outputs into a unified report?▼

Parallel AI agent orchestration synthesizes outputs by aggregating autonomous task results into a unified report, automatically highlighting areas of agreement and tension across diverse domain agents.

Do I need to hardcode agent personas to assemble an expert AI team on demand?▼

No, you do not need to hardcode agent personas; the system enforces non-hardcoded discovery, selecting domain-based agents dynamically to assemble teams for projects, site audits, or output synthesis.

How does this AI agent team orchestration handle failures during autonomous task execution?▼

During autonomous task execution, the orchestration system applies robust failure handling to manage errors across parallel agents, ensuring clean synthesis of final aggregated outputs despite individual agent issues.

What is the best way to assemble an engineering and marketing team to audit a project?▼

The best way to assemble an engineering and marketing team for a project audit is to select those specific domains, provide the audit task description, and run the parallel agent team for synthesized recommendations.