Loading...
Loading...
Found 130 Skills
Use when you need to review, improve, or write Java unit tests — including migrating from JUnit 4 to JUnit 5, adopting AssertJ for fluent assertions, structuring tests with Given-When-Then, ensuring test independence, applying parameterized tests, mocking dependencies with Mockito, verifying boundary conditions (RIGHT-BICEP, CORRECT, A-TRIP), leveraging JSpecify null-safety annotations, or eliminating testing anti-patterns such as reflection-based tests or shared mutable state. Part of the skills-for-java project
Avoid unnecessary useEffect in React components. Most uses of useEffect are anti-patterns — derived state, event-driven logic, data fetching, and external store subscriptions all have better, more idiomatic alternatives. Apply this skill when writing or reviewing React components that use useEffect.
Debug and fix polizy authorization issues. Use when permission checks fail unexpectedly, errors occur, or authorization behavior is confusing. Covers check algorithm, common issues, and anti-patterns.
Optimizes Snowflake SQL query performance from provided query text. Use when optimizing Snowflake SQL for: (1) User provides or pastes a SQL query and asks to optimize, tune, or improve it (2) Task mentions "slow query", "make faster", "improve performance", "optimize SQL", or "query tuning" (3) Reviewing SQL for performance anti-patterns (function on filter column, implicit joins, etc.) (4) User asks why a query is slow or how to speed it up
Refactor Django/Python code to improve maintainability, readability, and adherence to best practices. Transforms fat views, N+1 queries, and outdated patterns into clean, modern Django code. Applies Python 3.12+ features like type parameter syntax and @override decorator, Django 5+ patterns like GeneratedField and async views, service layer architecture, and PEP 8 conventions. Identifies and fixes anti-patterns including mutable defaults, bare exceptions, and improper ORM usage.
Core catalog of 8 critical Elixir/Phoenix anti-patterns covering error handling, separation of concerns, Ecto queries, and testing. Trigger: During Elixir code review, refactoring sessions, or when writing Phoenix/Ecto code.
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).
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.
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.
React patterns, anti-patterns, and performance optimization. Use when writing React components, reviewing React code, or debugging React issues.
Design thinking principles for distinctive interfaces. Covers aesthetic direction, anti-patterns, and avoiding generic AI-generated aesthetics.
Write behavior-focused tests following Testing Trophy model with real dependencies, avoiding common anti-patterns like testing mocks and polluting production code. Use when writing new tests, reviewing test quality, or improving test coverage.