deep-study

Automate deep-dive code analysis by running a single Bash command to extract project usage patterns, collect external resources, and generate structured NotebookLM artifacts plus multi-note Obsidian output for architectural review.

Yes, generate NotebookLM artifacts and Obsidian notes automatically by triggering a deep study on a code topic, which analyzes internal mechanics and compiles external resources into a cohesive knowledge base.

Understand framework mechanics in your project by triggering a natural-language request like "deep study X", which analyzes real implementation details and gathers external references into structured study notes.

Yes, deep study code analysis requires the notebooklm-py and playwright dependencies to automate resource gathering, generate NotebookLM artifacts, and produce multi-note Obsidian output for code comprehension.

Collect external resources by running the deep study Bash automation, which compiles official docs, blogs, videos, and open-source references into a cohesive knowledge base for the target code topic.

Avoid automated code analysis for implementation details when you lack the Bash environment or notebooklm-py and playwright dependencies, as the end-to-end deep-dive workflow relies on these components to generate structured outputs.