tldr

Semantic code search finds code meaning across large projects to quickly understand functionality. It generates context-aware summaries that preserve project context while significantly reducing token consumption during analysis.

Data flow and dependency tracing maps call graphs and structural relationships across a local project workspace. This generates architecture views and change-impact analyses to show how components interact within the codebase.

Yes, architecture overview extraction analyzes the local project workspace to generate structural views and diagnostics. It provides a high-level understanding of the codebase while remaining token-efficient by avoiding reading every individual file.

Yes, call-graph analysis requires a local project workspace and the llm-tldr tools to generate context-aware insights. The local environment is necessary to accurately trace dependencies, data flow, and extract structural architecture views.

Token-efficient codebase analysis relies on generated summaries and structural views rather than exhaustive file reading. While it effectively traces dependencies and data flow, highly granular implementation details may be abstracted away to preserve context and reduce token usage.