Agent skill · vasilyu1983
software-ai-integration
Applies production AI integration patterns for chat, structured output, guardrails, provider routing, and AI UX. Use when adding LLM-powered features to an application.
What it needs
About 12k tokens when loaded.
What this skill does
AI-Augmented Product Engineering Integrate LLMs and AI capabilities into production applications with clean architecture, cost discipline, and reliable user experience. Quick Reference Concern Defaults --- --- LLM API integration Anthropic SDK, OpenAI SDK, Vercel AI SDK Streaming responses SSE / ReadableStream + AI SDK streamText/streamObject Structured output JSON mode, tooluse/function calling, Zod schemas Chat interface AI SDK useChat hook, custom streaming UI AI-assisted forms Inline suggestions, auto-complete, content generation Guardrails Input/output filtering, content moderation, PII detection Cost management Token counting, caching (semantic + exact), model routing Multi-provider AI SDK provider abstraction, Portkey, LiteLLM, or a thin internal router Evaluation Human feedback, LLM-as-judge, A/B testing AI variants RAG in products Vector search + context injection (see also ai-rag for deeper patterns) When to Use This Skill Adding AI-powered features to an existing product (chat, generation, suggestions) Building streaming UI for LLM responses in web or mobile applications Implementing structured output with schema validation from LLM calls Designing cost control and caching strategies for AI features Building multi-provider fallback and model routing logic Implementing guardrails, content moderation, and safety layers Choosing AI UX patterns (loading states, regenerate, feedback, attribution) When NOT to Use This Skill LLM lifecycle management (fine-tuning, deployment, monitoring) → ai-llm Agent system architecture and orchestration → ai-agents Prompt engineering techniques and patterns → ai-prompt-engineering RAG system architecture (indexing, retrieval, chunking) → ai-rag ML model training and data science → ai-ml-data-science MLOps and model serving infrastructure → ai-mlops Building MCP servers and tool protocols → agents-mcp When NOT to Add AI At All Not every "AI feature" request should become one. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills vasilyu1983/software-ai-integration