Advanced mocking patterns for Jest and Vitest including module mocking, spies, and fake timers. PROACTIVELY activate for: (1) Module mocking, (2) Partial mocking with spies, (3) Mock lifecycle management, (4) Fake timers for time-dependent code, (5) Complex mock implementations. Triggers: "jest.mock", "vi.mock", "spyOn", "fakeTimers", "mockImplementation", "mockReturnValue", "mock lifecycle"
Ruff linting rules, security patterns, mypy type checking, and Python code review best practices. PROACTIVELY activate for: (1) Setting up Ruff linting, (2) Fixing security vulnerabilities, (3) Resolving mypy type errors, (4) Code review for anti-patterns, (5) Python best practices enforcement. Triggers: "ruff", "lint", "refactor", "security", "anti-pattern", "python review", "mypy", "type error"
Pytest fixtures, parametrization, mocking, and test organization patterns. PROACTIVELY activate for: (1) Writing pytest test suites, (2) Creating fixtures, (3) Implementing parametrized tests, (4) Mocking external dependencies, (5) Organizing test directories. Triggers: "pytest", "fixture", "parametrize", "mock", "python test", "unittest", "conftest"
Testing pyramid, quality metrics, and technical debt management strategies. PROACTIVELY activate for: (1) Designing testing strategy, (2) Evaluating code quality metrics, (3) Managing technical debt, (4) Analyzing test coverage approaches, (5) Calculating complexity. Triggers: "testing strategy", "testing pyramid", "quality metrics", "technical debt", "cyclomatic complexity", "maintainability", "coverage principles"
Test coverage analysis, gap identification, and coverage configuration for Python and JavaScript. PROACTIVELY activate for: (1) Analyzing coverage reports, (2) Identifying critical coverage gaps, (3) Configuring coverage tools, (4) Setting up CI/CD coverage checks, (5) Branch coverage analysis. Triggers: "coverage", "uncovered", "pytest-cov", "istanbul", "c8", "coverage report", "coverage gap"
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npx claudepluginhub agentient/vibekit --plugin quality-toolsModel Context Protocol (MCP) server development patterns
Frontend and backend performance optimization patterns
Multi-expert evaluation patterns for structured analysis, deliberation, and decision-making
[STUB] C4 diagrams, OpenAPI specs, architectural decisions
Research workflow tools: systematic interviewing, research brief design, and multi-source consolidation
Detect test smells, overmocking, flaky tests, and coverage issues. Analyze test effectiveness, maintainability, and reliability. Use when reviewing tests or improving test quality.
Analyze code coverage metrics, identify untested code, and generate comprehensive coverage reports
Use this agent for analyzing test results, synthesizing test data, identifying trends, and generating quality metrics reports. This agent specializes in turning raw test data into actionable insights that drive quality improvements. Examples:\n\n<example>\nContext: Analyzing test suite results
Configurable code review with radical candor - choose between harsh direct feedback or constructive caring criticism. Features Technical.md for project standards, architectural context analysis, actionable fixes, decision register for tracking review decisions, seamless todo integration, and live browser QA via Chrome.
Agentic Quality Engineering — AI-powered QE platform with 60 specialized agents, 75+ skills, sublinear coverage analysis, ReasoningBank pattern learning, and deep MCP integration for Claude Code and 11 coding agent platforms
Claude Code Skill for Ruby code quality analysis with RubyCritic. Model-invoked - Claude autonomously analyzes code quality, identifies smells, and provides refactoring insights.