review▌

Review pull requests against repository standards. Two-phase process: automated validation, then manual content review.

Systematic code review practices for constructive feedback, bug detection, and team knowledge sharing. \n \n Covers the complete review workflow: context gathering, high-level architecture assessment, line-by-line analysis, and decision-making with clear severity labeling (blocking, important, nit, suggestion) \n Includes language-specific patterns for Python, TypeScript, and JavaScript, plus specialized review techniques for security, testing, and architectural changes \n Provides templates, ch

Security-focused differential analysis of code changes with adaptive depth, blast radius calculation, and markdown reporting. \n \n Analyzes PRs, commits, and diffs across three codebase sizes (SMALL/MEDIUM/LARGE) with risk-first prioritization on auth, crypto, value transfer, and external calls \n Includes six-phase workflow: triage, code analysis, test coverage assessment, blast radius calculation, adversarial modeling, and comprehensive markdown report generation \n Detects security regressio

Comprehensive code reviews covering quality, security, performance, and best practices. \n \n Systematic review process across five dimensions: code quality, security vulnerabilities, performance, testing, and maintainability \n Includes initial assessment, detailed analysis guides, and constructive feedback frameworks \n Covers pull request analysis, coding standards compliance, and developer mentoring through structured review \n Best practices emphasize respectful feedback with explanations a

Systematic security code review identifying high-confidence vulnerabilities with data-flow verification. \n \n Focuses exclusively on HIGH CONFIDENCE findings: vulnerable patterns with confirmed attacker-controlled input, skipping theoretical issues and framework-mitigated code \n Requires codebase research before reporting: traces data flow, checks for validation/sanitization, and verifies exploitability rather than pattern-matching alone \n Covers 14 vulnerability categories (injection, XSS, a

Automated frontend code review against a multi-category checklist, supporting both pending-change and targeted file reviews. \n \n Triggers on user requests to review frontend files ( .tsx , .ts , .js ) and applies a canonical checklist across Code Quality, Performance, and Business Logic categories \n Supports two review modes: pending-change review for staged/working-tree files before commit, and file-targeted review for specific named files \n Flags violations with urgency metadata (Urgent vs

Comprehensive code analysis covering security vulnerabilities, performance bottlenecks, maintainability issues, and best practice violations. \n \n Scans for critical security flaws including SQL injection, XSS, authentication gaps, unsafe deserialization, and credential exposure \n Identifies performance problems such as N+1 queries, inefficient algorithms, memory leaks, and unnecessary re-renders \n Evaluates code quality across duplication, function length, cyclomatic complexity, naming clari

sentry-pr-code-review

Comprehensive security checklist and patterns for authentication, input validation, and sensitive data handling. \n \n Covers 10 core security areas: secrets management, input validation, SQL injection prevention, authentication/authorization, XSS prevention, CSRF protection, rate limiting, sensitive data exposure, blockchain wallet verification, and dependency security \n Includes concrete code examples for each vulnerability type, showing both unsafe and secure patterns with TypeScript and Nex

Perform comprehensive code reviews of GitLab merge requests, providing actionable feedback on code quality, security, performance, and best practices.