review▌

Use this skill whenever the user asks to review, analyze, or improve backend code (e.g., .py) under the api/ directory. Supports the following review modes:

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

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

Comprehensive security checklist covering secrets, input validation, authentication, and deployment readiness. \n \n Covers 10 critical security domains: 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 code examples for both unsafe and secure patterns across TypeScript, SQL, and configuration files \n Provides p

Find exploitable vulnerabilities in GitHub Actions workflows. Every finding MUST include a concrete exploitation scenario — if you can't build the attack, don't report it.

Expert code review of git changes with focus on SOLID violations, security risks, and actionable improvements. \n \n Analyzes current git diff for SOLID principle violations, architecture smells, and removal candidates using structured checklists \n Scans for security risks including injection, authentication gaps, race conditions, and unsafe deserialization with exploitability assessment \n Flags code quality issues: error handling gaps, N+1 queries, boundary condition bugs, and performance hot

Conduct comprehensive, systematic literature reviews using multiple academic databases and generate professionally formatted documents.

Code review framework following Sentry engineering practices for pull requests and code quality assessment. \n \n Covers six key problem areas: runtime errors, performance bottlenecks, side effects, backwards compatibility, ORM query issues, and security vulnerabilities \n Includes design assessment guidance for component interactions, architectural alignment, and requirement conflicts \n Requires appropriate test coverage across functional, integration, and end-to-end tests with verification of

Review Django code for validated performance issues. Research the codebase to confirm issues before reporting. Report only what you can prove.

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