feat: add intelligent AI context generator for enhanced code reviews

Add comprehensive context generation system that automatically analyzes projects and creates detailed context files to improve AI code review quality.

• Automatic Context Generation: ai-generate-context CLI tool that analyzes project structure, dependencies, and code patterns to generate comprehensive context files

• Hybrid LLM + Deterministic Analysis: Combines factual extraction (dependencies, file structure) with LLM-driven insights (architecture patterns, review focus areas)

• Manual Section Preservation: Users can add domain-specific context in designated manual sections that are preserved during automatic updates

• Partial Updates: Update specific sections (--section overview, tech_stack, structure, review_focus) while preserving others

• Multi-Provider Support: Works with Anthropic, Gemini, and Ollama AI providers

• Modular Design: Clean separation between facts extraction, code sampling, LLM analysis, and template rendering

• Section-Based: Pluggable section system for extensibility

• Template Engine: Markdown template system with manual section preservation

• Comprehensive Testing: 90%+ test coverage with unit and integration tests

• Project overview with purpose, type, and key dependencies

• Technology stack with versions and usage guidance for reviewers

• Architecture patterns and code organization principles

• Review focus areas tailored to the tech stack

• Manual sections for business logic, domain context, and special cases

• Seamlessly integrates with existing ai-code-review workflow

• Real project context file (.ai_review/project.md) serves as functional example

• Comprehensive documentation with setup guides and best practices

• Updated user guides to promote automatic context generation

This significantly enhances code review quality by providing AI reviewers with rich project context, leading to more accurate and relevant feedback.