Skill UI

This skill simulates a Principal Software Architect, specializing in designing, reviewing, and documenting high-level system architectures. It guides users through defining core system structures, evaluating complex technology trade-offs (e.g., ACID vs. eventual consistency), and planning for extreme scalability. Key outputs include comprehensive architecture diagrams (Mermaid) and formal Architecture Decision Records (ADRs), ensuring every major technical choice is documented with rationale and consequences.

This skill acts as a code education expert, specializing in breaking down complex code, algorithms, or system architecture. It transforms difficult technical concepts into understandable explanations using clear narratives, visual diagrams, and step-by-step breakdowns. Ideal for creating learning materials, onboarding guides, or detailed technical reviews for developers of all levels.

This comprehensive checklist guides users through the process of elevating CodeRabbit from an optional AI review tool to a mandatory merge gate in a production environment. It covers configuration auditing, technical validation (YAML syntax, review coverage), team onboarding, and setting up branch protection rules to ensure a smooth transition to required status.

Uses the Gemini API to autonomously plan, search, read, and synthesize detailed research reports with streaming status updates, structured outputs, and the ability to continue previous investigations for market analysis, technical reviews, or due diligence.

A comprehensive diagnostic toolkit designed for advanced troubleshooting of Lindy AI agents. This bundle systematically collects environment information (OS, Python, Node.js), tests API connectivity for webhooks and major integrations (Slack, Notion, Gmail), reviews task history, and guides the user in generating a full, structured support ticket for technical support.

A comprehensive technical audit checklist designed to validate the production readiness of OpenRouter API integrations. It covers critical areas including security practices (e.g., secret management, key rotation), system reliability (e.g., fallbacks, retries, timeouts), and observability requirements (e.g., structured logging, cost tracking). Use this guide before launching or during major operational reviews to ensure robust, scalable, and compliant deployments.

A comprehensive collection of battle-tested, structured prompts designed to elevate interactions with Large Language Models (LLMs). Covers advanced use cases including expert coding reviews, system architecture design, performance analysis, technical writing, and creative brainstorming, ensuring high-quality, actionable AI output.

Guidelines for responding to code review feedback with technical rigor: read every comment, restate requirements, verify against the codebase, ask clarifying questions, push back when technically needed, and implement fixes one at a time with testing.

This workflow guides developers through the systematic process of requesting and acting upon code reviews. It emphasizes the core principle of reviewing early and often, providing structured inputs (commits, requirements, implemented features) via a subagent to catch technical issues, ensure compliance with requirements, and maintain high code quality before merging.

Conduct rigorous, multi-database literature reviews with systematic workflows, verified citations in styles like APA or Nature, and AI-enhanced schematics for publication-ready markdown and PDF outputs in biomedical and technical domains.

A specialized assistant designed to act as a senior software architect. It critiques code generated by AI tools or rapidly prototyped code, moving beyond simple syntax checks. The auditor identifies deep structural flaws, technical debt, architectural weaknesses, and production risks (e.g., security vulnerabilities, poor error handling, tight coupling) that make code fragile or unsuitable for deployment.

Guides developers through receiving and responding to code review feedback: read carefully, restate requirements, verify against the codebase, evaluate correctness, push back with technical reasoning if needed, and implement one verified fix at a time.