Agent skill · software engineering · erichowens
ai-engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY
Why this skill is useful
Adds executable scripts for RAG architecture implementation and agent orchestration that aren't in public docs.
What it needs
Requires chroma, cohere, pinecone, weaviate installed locally. Requires openai account access. About 4k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
AI Engineer Expert in building production-ready LLM applications, from simple chatbots to complex multi-agent systems. Specializes in RAG architectures, vector databases, prompt management, and enterprise AI deployments. Quick Start Result: Production-ready AI chatbot in days, not weeks Core Competencies 1. RAG System Design Component Implementation Best Practices ----------- --------------- ---------------- Chunking Semantic, token-based, hierarchical 512-1024 tokens, overlap 10-20% Embedding OpenAI, Cohere, local models Match model to domain Vector DB Pinecone, Weaviate, Chroma, Qdrant Index by use case Retrieval Dense, sparse, hybrid Start hybrid, tune Reranking Cross-encoder, Cohere Rerank Always rerank top-k 2. LLM Application Patterns Chat with memory and context management Agentic workflows with tool use Multi-model orchestration (router + specialists) Structured output generation (JSON, XML) Streaming responses with error handling 3. Production Operations Token usage tracking and cost optimization Latency monitoring and caching strategies A/B testing for prompt versions Fallback chains and graceful degradation Security (prompt injection, PII handling) Architecture Patterns Basic RAG Pipeline Agent Architecture Multi-Model Router Implementation Checklist RAG System [ ] Document ingestion pipeline [ ] Chunking strategy (semantic preferred) [ ] Embedding model selection [ ] Vector database setup [ ] Retrieval with hybrid search [ ] Reranking layer [ ] Citation/source tracking [ ] Evaluation metrics (relevance, faithfulness) Production Readiness [ ] Error handling and retries [ ] Rate limiting [ ] Token tracking [ ] Cost monitoring [ ] Latency metrics [ ] Caching layer [ ] Fallback responses [ ] PII filtering [ ] Prompt injection guards Observability [ ] Request logging [ ] Response quality scoring [ ] User feedback collection [ ] A/B test framework [ ] Drift detection [ ] Alert thresholds Anti-Patterns Anti-Pattern: RAG Everything What it looks like: Using RAG …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/ai-engineer