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Context-driven code reviews prioritizing quality, security, testability, and maintainability. \n \n Organizes feedback into four priority levels: blockers (must fix), major issues (should fix), minor improvements, and suggestions, with clear templates and rationale for each \n Covers five core review areas: logic correctness, security risks, test coverage, performance issues, and error handling; explicitly excludes style nitpicking and formatting \n Recommends reviewing code in chunks under 400

Systematic literature reviews across multiple academic databases with verified citations and professional formatting. \n \n Searches PubMed, bioRxiv, arXiv, Semantic Scholar, and specialized databases (ChEMBL, KEGG, UniProt) via integrated skills; aggregates and deduplicates results across sources \n Structures reviews through seven phases: planning, multi-database search, screening, data extraction, thematic synthesis, citation verification, and PDF generation \n Verifies all DOIs and citations

Structured code review for security, performance, correctness, and maintainability across PR diffs and file changes. \n \n Audits security risks including SQL injection, XSS, CSRF, authentication flaws, and credential exposure \n Identifies performance issues like N+1 queries, memory leaks, algorithmic complexity, and resource leaks \n Checks correctness for edge cases, race conditions, error handling, and type safety gaps \n Works standalone with diffs and file paths; integrates with source con

Automated code review across security, performance, quality, and testing dimensions. \n \n Analyzes pull requests for four distinct review categories: security vulnerabilities (SQL injection, XSS, hardcoded secrets), performance issues (N+1 queries, memory leaks, missing caches), code quality (duplication, SRP violations, poor naming), and test coverage gaps \n Flags issues at three severity levels (critical, suggestions, nits) with explanations and suggested fixes for each finding \n Includes a

$22

Comprehensive code review for diffs with auto-detected domain-specific checks. \n \n Analyzes changed code for security vulnerabilities, anti-patterns, quality issues, and breaking changes across generic, frontend, and backend domains \n Auto-detects domain (frontend, backend, CLI, config) from file paths and applies relevant checklists \n Generates structured markdown reports with severity levels, file locations, code snippets, and actionable recommendations \n Performs impact analysis to ident

AI-powered code review using CodeRabbit, triggered on explicit request or autonomously when quality/security issues are detected. \n \n Identifies bugs, security vulnerabilities, and quality risks; groups findings by severity (Critical, Warning, Info) \n Supports reviewing staged, committed, or all changes; can compare against specific branches or commit hashes \n Offers two output modes: --plain for detailed feedback with fix suggestions, or --prompt-only for minimal agent-optimized output \n E

Review Google Meet attendance records and participant session durations. \n \n Requires the gws-meet skill to access Google Meet conference data \n Three-step workflow: list recent conferences, retrieve participant details, and view individual session timestamps and durations \n Returns tabular output for easy review of who attended and how long they stayed \n

Review a web app or page for visual design quality. This is not a UX audit (usability, workflow, friction) — this checks whether the design is professional, consistent, and polished.

Analyze, audit, and improve project structure.