genai-integrationlisted

# Purpose Teach the agent how to handle GenAI integration tasks — selecting models, building prompt templates, RAG pipelines, cost optimization, and validation workflows. # When to Apply Use this Skill when the user asks to: - integrate a GenAI API into an application - design RAG workflows, embeddings pipelines, or agents - build prompt templates or schema-validated prompts - write automation for cost or token optimization - add testing, logging, or observability around GenAI tasks # Instructions 1. **Detect Task Intent** - Identify if the request is about GenAI model selection, API integration, workflow design, or optimization. - If the task involves specific frameworks (Node/Python, serverless, Vercel/AWS), include relevant context. 2. **Model & Provider Guidance** - Recommend models according to cost, latency, context length, and compliance needs. - Prefer structured outputs (JSON schemas) and function/tool calling where appropriate. 3. **Prompt Engineering** - Generate prompt templates: system, developer, and user layers. - Use few-shot examples and explicit output schemas in prompts. 4. **RAG & Embeddings** - Break documents into chunks with semantic similarity filtering. - Outline vector store choice and search parameters (faiss/pinecone/weaviate). 5. **Agent Workflows** - If task requires agents, design tool use steps, fallback logic, and task decomposition. - Provide stepwise workflows for planning and execution. 6. **Cost & Toke