architecture-pattern-selector
π―Skillfrom alirezarezvani/claude-cto-team
Recommends optimal software architecture patterns based on project requirements, tech stack, and scalability needs
Same repository
alirezarezvani/claude-cto-team(14 items)
Installation
npx vibeindex add alirezarezvani/claude-cto-team --skill architecture-pattern-selectornpx skills add alirezarezvani/claude-cto-team --skill architecture-pattern-selector~/.claude/skills/architecture-pattern-selector/SKILL.mdSKILL.md
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AI-powered CTO team for Claude Code with custom subagents providing strategic technical leadership, system architecture design, and brutally honest feedback on technical decisions.
Your personal CTO Team for Claude Code . These Subagents will help you challenging yourself while you plan and execute.
A set of three specialized AI subagents for Claude Code that provide strategic technical leadership, system architecture design, and honest feedback on technical decisions. Includes a CTO orchestrator, architect, and strategic mentor agent.
Analyzes system architecture and recommends scalable design patterns, infrastructure optimizations, and performance strategies for cloud and distributed systems
Streamlines communication by systematically breaking down complex queries, identifying ambiguities, and requesting precise clarifications from users.
Critically examines and deconstructs underlying assumptions in project plans, design proposals, and strategic decisions to reveal hidden biases and potential blind spots.
Identifies and flags common code antipatterns in software design, providing actionable recommendations for improving code quality and maintainability.
Automates computer vision tasks with pre-trained models for image classification, object detection, segmentation, and advanced feature extraction
Analyzes API and HTTP requests to identify potential performance bottlenecks, security vulnerabilities, and optimization opportunities in network communication
Crafts precise, role-specific prompts to delegate complex tasks effectively across AI team members with clear context and expectations