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Write-time code quality enforcement with auto-formatting, linting, and Claude-powered subprocess fixes on every file edit. \n \n Three-phase architecture: silent auto-formatting, violation collection, and tiered model routing (Haiku for style, Sonnet for complexity, Opus for types) to fix unfixable issues \n Supports Python, TypeScript, Shell, YAML, JSON, TOML, Markdown, and Dockerfile with 20+ linters including ruff, biome, shellcheck, and hadolint \n Config protection via PreToolUse and Stop h

Comprehensive SQL security, performance, and quality analysis across MySQL, PostgreSQL, SQL Server, and Oracle databases. \n \n Analyzes SQL injection vulnerabilities, access control issues, and sensitive data exposure with parameterized query examples for each database platform \n Reviews query performance through index strategy, join optimization, and anti-pattern detection (N+1 queries, function misuse in WHERE clauses, overuse of DISTINCT) \n Evaluates code quality including naming conventio

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

You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create actionable remediation plans.

Code Components: Custom React components added to canvas. Support addPropertyControls.

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

Production-grade code standards emphasizing correctness, crash-safety, and Rust idioms over convenience. \n \n Prioritizes data integrity: crash on invalid state rather than corrupt data; assert invariants aggressively; handle all errors explicitly \n Rust-specific patterns: make illegal states unrepresentable, use exhaustive pattern matching and enums over strings, minimize allocations \n Comments document why , not what ; avoid temporal markers, AI conversation references, and code-repeating e

Build AI-powered software with TypeSafe, using AI intelligence as programming primitives.

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