How does the skill identify my technology stack?

It analyzes manifest files like package.json, requirements.txt, or go.mod, along with configuration files like tsconfig.json and framework-specific settings.

Can I customize the types of agents generated?

Yes, the skill uses specific mapping patterns for Architecture, Tech-Stack, Domain, and DevOps roles which can be customized via the provided templates.

How many agents should I generate for my project?

The skill suggests team sizes based on project complexity: 2-3 agents for small projects, 4-5 for mid-sized ones, and up to 8 for large monorepos.

What is a project-aware agent?

A project-aware agent is a Claude Code configuration that includes specific knowledge about your project, such as directory structures, framework versions, and coding conventions.

Agent Team Generator

Name: Agent Team Generator
Author: Cpicon

byCpicon

Productividad y Flujo de Trabajo

Automatically creates a specialized team of project-aware Claude Code agents by analyzing your codebase's architecture and technology stack.

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The Agent Team Generator skill empowers developers to build highly context-aware AI teams specifically tailored to their unique projects. By analyzing directory structures, configuration files, and domain logic, it generates specialized agents for distinct roles such as Architecture Expert, Tech-Stack Specialist, and Domain Guru. This ensures that every AI interaction is grounded in the actual patterns, libraries, and conventions of your codebase, significantly improving the accuracy and relevance of AI-assisted development across complex repositories.

Características Principales

Automated codebase analysis for tech stack and architecture discovery
Template-based generation of specialized agents with distinct roles and triggers
0 GitHub stars
Project-aware system prompt creation including file paths and naming conventions
Standardized color coding and trigger mapping for organized agent teams
Dynamic team sizing recommendations based on project complexity and file count

Casos de Uso

Setting up a multi-agent workflow for a monorepo or microservices architecture
Onboarding an AI assistant to a large legacy codebase with unique internal patterns
Standardizing AI interactions across a development team using shared agent definitions

Acerca de

Características Principales

Automated codebase analysis for tech stack and architecture discovery
Template-based generation of specialized agents with distinct roles and triggers
0 GitHub stars
Project-aware system prompt creation including file paths and naming conventions
Standardized color coding and trigger mapping for organized agent teams
Dynamic team sizing recommendations based on project complexity and file count

Casos de Uso

Setting up a multi-agent workflow for a monorepo or microservices architecture
Onboarding an AI assistant to a large legacy codebase with unique internal patterns
Standardizing AI interactions across a development team using shared agent definitions