Knowledge base integration and token efficiency for `dbskill` within Claude Code.

This issue details critical problems with knowledge base integration and token efficiency in `dbskill` for Claude Code. The `npx skills add` command fails to install the full knowledge base, and manual integration of large files like `atoms.jsonl` (2.7MB) exceeds Claude Code's context window, leading to prohibitive token costs without a built-in RAG engine. This exposes a fundamental limitation in current AI agent architectures for handling extensive external knowledge efficiently. The proposed solution—leveraging NotebookLM for knowledge retrieval and modifying skill prompts to trigger it—highlights a workaround for token management and RAG functionality. This indicates a strong market need for native, cost-effective RAG capabilities within AI agent frameworks to enable deep, context-rich interactions without excessive token consumption.