Agent skill · software engineering · aiskillstore
ai-native-development
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
Why this skill is useful
Adds detailed implementation guides and executable patterns for RAG systems, embeddings, and agentic workflows that extend AI capabilities significantly.
What it needs
Requires chroma, pinecone, qdrant, weaviate installed locally. About 9k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
AI-Native Development Overview AI-Native Development focuses on building applications where AI is a first-class citizen, not an afterthought. This skill provides comprehensive patterns for integrating LLMs, implementing RAG (Retrieval-Augmented Generation), using vector databases, building agentic workflows, and optimizing AI application performance and cost. When to use this skill: Building chatbots, Q&A systems, or conversational interfaces Implementing semantic search or recommendation engines Creating AI agents that can use tools and take actions Integrating LLMs (OpenAI, Anthropic, open-source models) into applications Building RAG systems for knowledge retrieval Optimizing AI costs and latency Implementing AI observability and monitoring --- Why AI-Native Development Matters Traditional software is deterministic; AI-native applications are probabilistic: Context is Everything: LLMs need relevant context to provide accurate answers RAG Over Fine-Tuning: Retrieval is cheaper and more flexible than fine-tuning Embeddings Enable Semantic Search: Move beyond keyword matching to understanding meaning Agentic Workflows: LLMs can reason, plan, and use tools autonomously Cost Management: Token usage directly impacts operational costs Observability: Debugging probabilistic systems requires new approaches Prompt Engineering: How you ask matters as much as what you ask --- Core Concepts 1. Embeddings & Vector Search Embeddings are vector representations of text that capture semantic meaning. Similar concepts have similar vectors. Key Capabilities: Convert text to high-dimensional vectors (1536 or 3072 dimensions) Measure semantic similarity using cosine similarity Find relevant documents through vector search Batch process for efficiency Detailed Implementation: See references/vector-databases.md for: OpenAI embeddings setup and batch processing Cosine similarity algorithms Chunking strategies (500-1000 tokens with 10-20% overlap) 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/ai-native-development