review▌

These are idiomatic FastAPI patterns that may appear problematic but are correct:

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

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.

Simple, pragmatic, opinionated. Only what matters for writing production-grade PostgreSQL queries.

Simple, pragmatic, opinionated. Only what matters for writing production-grade SQLAlchemy and Alembic code.

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

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

This skill enables AI agents to perform a comprehensive visual design and aesthetics evaluation of digital interfaces, analyzing elements like typography, color palettes, spacing, visual hierarchy, and overall design quality.

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:

Cross-model validation using the codex binary directly. Claude writes code, Codex reviews it — different architecture, different training distribution, no self-approval bias.