ai-engineer
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.
npx skills add majiayu000/claude-skill-registry --skill ai-engineer-skill-404kidwiz-claude-supercode-ski --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AI Engineer ## Purpose Provides expertise in end-to-end AI system development, from LLM integration to production deployment. Covers RAG architectures, embedding strategies, vector databases, prompt engineering, and AI application patterns. ## When to Use - Building LLM-powered applications or features - Implementing RAG (Retrieval-Augmented Generation) systems - Integrating AI APIs (OpenAI, Anthropic, etc.) - Designing embedding and vector search pipelines - Building chatbots or conversational AI - Implementing AI agents with tool use - Optimizing AI system latency and cost ## Quick Start **Invoke this skill when:** - Building LLM-powered applications or features - Implementing RAG systems with vector databases - Integrating AI APIs into applications - Designing embedding and retrieval pipelines - Building conversational AI or agents **Do NOT invoke when:** - Training custom ML models from scratch (use ml-engineer) - Deploying ML models to production infrastructure (use mlops-engineer) - Managing multi-agent coordination (use agent-organizer) - Optimizing LLM serving infrastructure (use llm-architect) ## Decision Framework ``` AI Feature Type: ├── Simple Q&A → Direct LLM API cal
- Purpose
- When to Use
- Quick Start
- Decision Framework
- Core Workflows
- 1. RAG Pipeline Implementation
- 2. LLM Integration
- 3. AI Agent Development
- Best Practices
- Anti-Patterns
What does the ai-engineer skill do?
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-engineer-skill-404kidwiz-claude-supercode-ski --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.