review▌

Structured code review framework covering quality, security, performance, and testing standards. \n \n Provides eight-step review methodology: context understanding, high-level architecture assessment, detailed code inspection, security audit, performance analysis, testing validation, documentation check, and constructive feedback delivery \n Covers SOLID principles, naming conventions, error handling, input validation, authentication/authorization, SQL injection and XSS prevention, and resource

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

Conduct a systematic literature review on "$ARGUMENTS" using the paper and paper-search CLI tools.

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:

Dispatch code review subagents with focused context to catch issues before they compound. \n \n Integrates with subagent-driven development workflows, triggering review after each task or before merging to main \n Provides code-reviewer subagent with precise git SHAs, implementation details, and requirements, keeping reviewer focused on work product rather than session history \n Categorizes feedback into Critical (fix immediately), Important (fix before proceeding), and Minor (note for later) s

Multi-dimensional code review with quality gates. Every change gets reviewed before merge — no exceptions. Review covers five axes: correctness, readability, architecture, security, and performance.

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

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

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

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.