Implements sophisticated file fingerprinting that distinguishes structural changes from cosmetic ones (like whitespace). Uses content hashing with normalization and provides change analysis to determine appropriate update strategies. This self-contained module demonstrates non-obvious skill in change detection and could be extracted for any incremental analysis system.

Defines the LanguageExtractor interface and provides shared tree-sitter traversal utilities that all language-specific extractors inherit. Creates a consistent, extensible pattern for adding new language support with minimal boilerplate. Demonstrates skill in designing maintainable multi-language analysis systems through abstraction and shared utilities.

Determines the appropriate update type (SKIP, PARTIAL_UPDATE, ARCHITECTURE_UPDATE, FULL_UPDATE) by analyzing file changes, including directory modifications and deletions. Implements intelligent update strategies that minimize unnecessary work while ensuring correctness. Shows non-obvious skill in balancing efficiency with completeness in incremental systems.

Implements a multi-pass normalization pipeline that fixes ID inconsistencies, normalizes complexity values, and rewrites edges to maintain graph integrity. Runs before upstream sanitization to ensure data quality. Demonstrates sophisticated graph data management skills and could be reused in any graph processing pipeline requiring consistency guarantees.