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Found 160 Skills
Use when reviewing code for anti-patterns. Keywords: anti-pattern, common mistake, pitfall, code smell, bad practice, code review, is this an anti-pattern, better way to do this, common mistake to avoid, why is this bad, idiomatic way, beginner mistake, fighting borrow checker, clone everywhere, unwrap in production, should I refactor, 反模式, 常见错误, 代码异味, 最佳实践, 地道写法
Review Encore.ts code for best practices and anti-patterns.
Expert patterns for Godot AutoLoad (singleton) architecture including global state management, scene transitions, signal-based communication, dependency injection, autoload initialization order, and anti-patterns to avoid. Use for game managers, save systems, audio controllers, or cross-scene resources. Trigger keywords: AutoLoad, singleton, GameManager, SceneTransitioner, SaveManager, global_state, autoload_order, signal_bus, dependency_injection.
Stop your AI agent from generating Tailwind CSS v3 code. Rules for v4 syntax, CSS-first config, modern utility patterns, and common anti-patterns.
Expert code reviewer for TypeScript + React 19 applications. Use when reviewing React code, identifying anti-patterns, evaluating state management, or assessing code maintainability. Triggers: code review requests, PR reviews, React architecture evaluation, identifying code smells, TypeScript type safety checks, useEffect abuse detection, state management review.
Use when clarifying fuzzy boundaries, defining quality criteria, teaching by counterexample, preventing common mistakes, setting design guardrails, disambiguating similar concepts, refining requirements through anti-patterns, creating clear decision criteria, or when user mentions near-miss examples, anti-goals, what not to do, negative examples, counterexamples, or boundary clarification.
Analyzes and improves LLM prompts and agent instructions for token efficiency, determinism, and clarity. Use when (1) writing a new system prompt, skill, or CLAUDE.md file, (2) reviewing or improving an existing prompt for clarity and efficiency, (3) diagnosing why a prompt produces inconsistent or unexpected results, (4) converting natural language instructions into imperative LLM directives, or (5) evaluating prompt anti-patterns and suggesting fixes. Applies to all LLM platforms (Claude, GPT, Gemini, Llama).
Generic test writing discipline: test quality, real assertions, anti-patterns, and rationalization resistance. Use when writing tests, adding test coverage, or fixing failing tests for any language or framework. Complements language-specific skills.
Audits code for design pattern opportunities and anti-patterns — identifies places where a specific GoF or architectural pattern would solve an observable problem, and flags misapplied patterns that add complexity without benefit. Generates fix prompts. Trigger phrases: "design patterns", "pattern check", "pattern review", "refactoring patterns", "pattern analysis".
Detects code smells and anti-patterns — long methods, large classes, feature envy, data clumps, primitive obsession, dead code, magic numbers, deep nesting, and more. Uses configurable thresholds from .codeprobe-config.json when available. Trigger phrases: "code smells", "smell check", "anti-patterns", "clean code review".
Turborepo monorepo architecture decisions and anti-patterns. Use when: (1) choosing between monorepo vs polyrepo, (2) deciding when to split packages, (3) debugging cache misses, (4) setting package boundaries, (5) avoiding circular dependencies. NOT for CLI syntax (see turbo --help). Focuses on architectural decisions that prevent monorepo sprawl and maintenance nightmares. Triggers: turborepo, monorepo, package boundaries, when to split packages, turbo cache miss, circular dependency, workspace organization, task dependencies.
AI design workflow with DESIGN.md, anti-patterns, and optional Stitch MCP