By VorobiovD
Automated code review with verification, pattern learning, and team knowledge. 5 specialized agents + Codex, wiki-based self-learning, re-review tracking, self-review with auto-fix.
Fetch REVIEW.md from the wiki, clean it up using AI, generate REVIEW-HISTORY.md from PR comment history, and push both back.
Review code using specialized agents. If a PR number is given, review that PR. If no arguments, auto-detect: review the current branch's PR if one exists, or self-review local changes if not.
Review code changes for quality, design, and project conventions. For security checks, use security-auditor.
Review code changes through the lens of git history — blame, churn, previous PR feedback, and authorship patterns.
Verify code review findings against actual source code. Filter false positives, score confidence, and confirm real issues.
Audit code changes for security vulnerabilities, data exposure, injection risks, auth gaps, and compliance concerns.
Review changed code for reuse, quality, and efficiency. Report findings only.
Uses power tools
Uses Bash, Write, or Edit tools
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npx claudepluginhub vorobiovd/air --plugin airMulti-lens code review pipeline: deep review (Claude or Codex), automated fix loop, interactive walkthrough, manual promote, external-finding injection.
Review pull requests with structured analysis and approve with confidence
pair-review app integration — Open PRs and local changes in the pair-review web UI, run server-side AI analysis, and address review feedback. Requires the pair-review MCP server.
Automated code review for pull requests using multiple specialized agents with confidence-based scoring
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Comprehensive PR review agents specializing in comments, tests, error handling, type design, code quality, and code simplification