code-review-mastery

Automated code review for local git changes analyzes staged and unstaged diffs to produce structured MAJOR and MINOR findings. It reads project configurations like CLAUDE.md and lint rules to calibrate severity and guide pre-commit fixes.

Project-aware static analysis for git diffs evaluates local code changes using your repository's specific lint rules, formatting configs, and framework patterns. This contextual review generates structured findings to help prioritize fixes before a commit.

AI-assisted code review works across Claude Code, Gemini CLI, OpenAI Codex, and other compatible agents. It delivers consistent project-aware analysis and structured findings for interactive review across these supported platforms.

Reviewing staged and unstaged changes with an AI assistant involves triggering a diff review. The assistant gathers changes using git diff, reads local project conventions, and outputs structured MAJOR and MINOR findings for interactive review.

The best way to prioritize pre-commit code fixes is generating structured MAJOR and MINOR findings from local git changes. Project-aware analysis calibrates severity using lint configs and conventions to guide your interactive review process.

Lint configurations are needed for AI-assisted code review to calibrate severity and provide project-aware analysis. Reading these configs alongside local git diffs allows the review to produce findings aligned with your specific framework patterns and conventions.