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Found 817 Skills
Comprehensive checklist for conducting thorough code reviews covering functionality, security, performance, and maintainability
Best practices for working with Cursor. Use when learning how to effectively use Cursor features or optimizing your workflow.
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.
SOLID principles, design patterns, DRY, KISS, and clean code fundamentals. Use when reviewing architecture, checking code quality, refactoring, or discussing design decisions. Triggers on "review architecture", "check code quality", "SOLID principles", "design patterns", or "clean code".
Assess, quantify, and prioritize technical debt using code analysis, metrics, and impact analysis. Use when planning refactoring, evaluating codebases, or making architectural decisions.
Analyze code complexity, cyclomatic complexity, maintainability index, and code churn using metrics tools. Use when assessing code quality, identifying refactoring candidates, or monitoring technical debt.
Configures ESLint and Prettier for consistent code quality with TypeScript, React, and modern best practices. Use when users request "ESLint setup", "Prettier config", "linting configuration", "code formatting", or "lint rules".
Code review guidelines covering code quality, security, and best practices.
Review a project's PRs to check for issues detected in code review by Seer Bug Prediction. Use when asked to review or fix issues identified by Sentry in PR comments, or to find recent PRs with Sentry feedback.
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 acti
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.