By tonone-ai
Map backend routes, middleware stacks, database models, dependencies, and auth mechanisms, and assess code quality for project takeover
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin spine-reconAPI and backend code review — REST conventions, auth, validation, error handling, pagination, rate limiting, test coverage. Use when asked to "review this API", "code review", "review backend", or "pre-launch backend check".
Use this agent when designing APIs, building server-side logic, implementing databases, or architecting scalable backend systems. This agent specializes in creating robust, secure, and performant backend services. Examples:\n\n<example>\nContext: Designing a new API\nuser: "We need an API for our social sharing feature"\nassistant: "I'll design a RESTful API with proper authentication and rate limiting. Let me use the backend-architect agent to create a scalable backend architecture."\n<commentary>\nAPI design requires careful consideration of security, scalability, and maintainability.\n</commentary>\n</example>\n\n<example>\nContext: Database design and optimization\nuser: "Our queries are getting slow as we scale"\nassistant: "Database performance is critical at scale. I'll use the backend-architect agent to optimize queries and implement proper indexing strategies."\n<commentary>\nDatabase optimization requires deep understanding of query patterns and indexing strategies.\n</commentary>\n</example>\n\n<example>\nContext: Implementing authentication system\nuser: "Add OAuth2 login with Google and GitHub"\nassistant: "I'll implement secure OAuth2 authentication. Let me use the backend-architect agent to ensure proper token handling and security measures."\n<commentary>\nAuthentication systems require careful security considerations and proper implementation.\n</commentary>\n</example>
Comprehensive backend development toolkit combining API design, database architecture, security, and scalability. Includes backend lead agent for holistic backend decisions.
Mindful AI coding framework — discipline over cleverness. Skill + 21 slash commands + 8 specialist agents + 5 runtime hooks + 15 default checklists + Master Orchestrator + Gravity hub. Works on any model tier (Opus/Sonnet/Haiku). Integrates Claude Design for visual work.
Use this agent when you need to design scalable architecture and folder structures for new features or projects. Examples include: when starting a new feature module, refactoring existing code organization, planning microservice boundaries, designing component hierarchies, or establishing project structure conventions. For example: user: 'I need to add a user authentication system to my app' -> assistant: 'I'll use the code-architect agent to design the architecture and folder structure for your authentication system' -> <uses agent>. Another example: user: 'How should I organize my e-commerce product catalog feature?' -> assistant: 'Let me use the code-architect agent to design a scalable structure for your product catalog' -> <uses agent>.
Google Gemini CLI for second opinions, architectural advice, code reviews, security audits. Leverage 1M+ context for comprehensive codebase analysis via command-line tool.
Design and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".