單元測試

Use when synthesizing information from multiple sources (literature review, stakeholder feedback, research findings, data from different systems), creating or evaluating analogies for explanation or problem-solving (cross-domain transfer, "X is like Y", structural mapping), combining conflicting viewpoints into unified framework, identifying patterns across disparate sources, finding creative solutions by transferring principles from one domain to another, testing whether analogies hold (surface vs deep similarities), or when user mentions "synthesize", "combine sources", "analogy", "like", "similar to", "transfer from", "integrate findings", "what's it analogous to".

Testing React Native. Use when writing tests, reviewing test coverage, or setting up testing.

Run Mojo tests using mojo test command. Use when executing tests or verifying test coverage.

Comprehensive web page validation with authentication, screenshot capture, mobile testing, and enhanced error detection

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions

Use this skill when the user wants to run performance benchmarks, compare languages, or test the compute-average-income implementations.

Compare a GitHub repository with connectrpc-axum. This skill should be used when the user provides a GitHub repository URL and wants to compare it with the current project. Analyzes user features, technical implementation, architecture patterns, code quality, tests, and documentation. Outputs comparison to docs/guide/compare/ directory.

Research and compile the latest AI news from across the industry. Use this skill when asked to find AI news, get AI updates, research what's happening in AI, check for AI announcements, or gather intelligence on AI companies. Triggers include requests for "AI news", "latest AI developments", "what's new in AI", "AI industry updates", or news about specific AI companies (OpenAI, Anthropic, Google, Microsoft, Meta, Amazon, Nvidia, xAI, Mistral, Cohere, Apple, Salesforce).

Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps. Invoke when user mentions abstraction levels, making concepts concrete, or explaining at different depths.

Measure model performance on test datasets. Use when assessing accuracy, precision, recall, and other metrics.

Use when facing decisions with multiple legitimate perspectives and inherent tensions. Invoke when stakeholders have competing priorities (growth vs. sustainability, speed vs. quality, innovation vs. risk), need to pressure-test ideas from different angles before committing, exploring tradeoffs between incompatible values, synthesizing conflicting expert opinions into coherent strategy, or surfacing assumptions that single-viewpoint analysis would miss.

Run code snippets in 30+ programming languages including JavaScript, Python, TypeScript, Java, C, C++, Go, Rust, Ruby, PHP, and more. Use when the user wants to execute code, test algorithms, verify output, run scripts, or check code behavior. Supports both interpreted and compiled languages.

Create starter code from templates. Use when setting up new modules or test files.

Estrategia de Testes React. Use when writing tests, reviewing test coverage, or setting up testing.

Estrategia de Testing React. Use when writing tests, reviewing test coverage, or setting up testing.

Use when optimizing multi-factor systems with limited experimental budget, screening many variables to find the vital few, discovering interactions between parameters, mapping response surfaces for peak performance, validating robustness to noise factors, or when users mention factorial designs, A/B/n testing, parameter tuning, process optimization, or experimental efficiency.

Use when creating, editing, or breaking down backlog tasks. Invoked for task management, feature decomposition, writing acceptance criteria, and ensuring tasks follow atomic, testable, independent guidelines.

Use this skill when configuring, building, or rebuilding CPython from source, running tests, or debugging test failures. Covers ./configure with --with-pydebug, make commands, ccache for faster rebuilds, Argument Clinic regeneration (make clinic), unittest-based testing with python -m test (NOT pytest), --match filtering, code coverage collection, and platform-specific build paths (Linux vs macOS).

Analyze test cases against specifications to find ambiguous assumptions. Use when tests might be making assumptions not explicitly defined in the spec. Invoke with /test-ambiguity-detector <problem> <checkpoint>.

Systematic bug investigation and fixing workflow with root cause analysis, reproduction tests, and verified fixes. Orchestrates the multi-phase process: gather context, investigate, reproduce, fix, verify.