Total 43,574 skills, Code Quality has 2034 skills
Showing 12 of 2034 skills
Modern TypeScript patterns your AI agent should use. Strict mode, discriminated unions, satisfies operator, const assertions, and type-safe patterns for TypeScript 5.x.
TypeScript best practices and patterns for writing type-safe, maintainable code. Use when working with TypeScript files, configuring tsconfig, defining interfaces/types, implementing error handling, writing generics, or setting up type-safe communication patterns. Includes patterns for discriminated unions, type guards, utility types, and more.
Perform exhaustive code reviews using multi-agent analysis, ultra-thinking, and worktrees
Use this skill for mathematical code verification. Use when reviewing math-heavy code, verifying algorithm correctness, checking numerical stability, aligning with mathematical standards. Do not use when general algorithm review - use architecture-review. DO NOT use when: performance optimization - use parseltongue:python-performance.
Read open review comments and resolve them by making code fixes
Devil's Advocate stress-testing for code, architecture, PRs, and decisions. Surfaces hidden flaws through structured adversarial analysis with metacognitive depth. Use for high-stakes review, stress-testing choices, or when the user wants problems found deliberately. NOT for routine code review (use engineering:code-review). Triggers on "스트레스 테스트", "stress test", "devil's advocate", "반론", "이거 괜찮아", "문제 없을까", "깊은 리뷰", "critical review", "adversarial".
Golang code style, formatting and conventions. Use when writing code, reviewing style, configuring linters, writing comments, or establishing project standards.
Agent skill for reviewer - invoke with $agent-reviewer
Implements error handling patterns, structured logging, retry strategies, circuit breakers, and graceful degradation. Use when designing error handling, setting up logging, implementing retries, adding error tracking, or when asked about error boundaries, log aggregation, alerting, or resilience patterns.
Reviews DataHub connector implementations against 22 golden standards for compliance, code quality, silent failures, test coverage, type design, and merge readiness. Use when reviewing connector code, checking a PR, auditing a connector implementation, or verifying connector standards compliance.
Review staged git changes against an issue. Produces a structured improvement plan — no edits applied. Also identifies test file compaction opportunities. Use when asked to check, review, or validate staged work before committing. Part 2 of 3 in the issue-review-two-phases workflow.
Analyzes codebases to identify refactoring opportunities based on Martin Fowler's catalog of code smells and refactoring techniques. Detects duplicated code, high coupling, complex conditionals, primitive obsession, long functions, and other structural issues. Produces a structured refactoring report with prioritized findings saved to docs/_refacs/. Use when auditing code quality, preparing for a refactoring sprint, or reviewing architectural health. Don't use for style/formatting issues, performance optimization, or security audits.