ai-engineer-expert
About
This skill provides expert guidance for implementing and deploying production-ready AI systems with a focus on LLM integration. It covers core areas like prompt engineering, RAG, vector databases, and deployment strategies including API design and monitoring. Use it when building, optimizing, or scaling AI applications that require robust, real-world engineering practices.
Quick Install
Claude Code
Recommendednpx skills add majiayu000/claude-skill-registry -a claude-code/plugin add https://github.com/majiayu000/claude-skill-registrygit clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/ai-engineer-expertCopy and paste this command in Claude Code to install this skill
GitHub Repository
Related Skills
when-optimizing-prompts-use-prompt-architect
OtherPrompt Architect is a framework for developers to systematically analyze, refine, and optimize prompts using evidence-based techniques. It helps improve AI response quality and consistency by identifying anti-patterns and validating changes through A/B testing. Use it when you need to refactor an underperforming prompt or design a new, effective one from scratch.
github-release-management
OtherThis Claude Skill automates GitHub release workflows using AI swarm coordination for versioning, testing, deployment, and rollback. It's ideal for developers needing automated CI/CD pipelines with intelligent changelog generation and multi-platform deployment management. Use it when you want to orchestrate complex releases with minimal manual intervention.
n8n-integration-testing-patterns
OtherThis skill provides testing patterns for n8n workflow integrations with external APIs. It covers API contract validation, authentication flows, rate limit handling, and error scenario testing. Use it when developing or validating n8n node integrations to ensure reliability.
vertex-agent-builder
MetaThis skill provides production-ready scaffolding for building and deploying generative AI agents on Google Cloud's Vertex AI platform. It enables developers to quickly implement agents with RAG, function calling, and multi-modal capabilities using Gemini models. Use this when you need to deploy scalable, enterprise-ready AI agents on Google Cloud infrastructure.
