test-review

Review test code for quality, flakiness, and design risks.

Updated May 17, 2022
One-click install
npx skills add https://github.com/kanade0404/dotfiles --skill test-review-kanade0404
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: test-review
Source: https://github.com/kanade0404/dotfiles/tree/main/.opencode/skills/test-review
Command: npx skills add https://github.com/kanade0404/dotfiles --skill test-review-kanade0404

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you review test code with a consistent, high-signal checklist so regressions, brittle assertions, and weak coverage are caught before merge.

Core Features & Use Cases

  • Test quality review: Checks naming, structure, assertions, and readability for unit, integration, workflow, and end-to-end tests.
  • Smell and risk detection: Flags fragile tests, over-mocking, flaky patterns, conditional test logic, and AI-generated test anti-patterns.
  • Coverage and seam analysis: Verifies seams, boundaries, database and authorization coverage, and whether tests actually prove the intended behavior.
  • Use case: Use this Skill when a pull request adds or changes tests and you need a concise review with concrete findings and fixes.

Quick Start

Ask this Skill to review the test files in your diff and return prioritized findings in the standard Test Review format.

Frequently Asked Questions about test-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review test code for flakiness and over-mocking risks?

Test code review detects flakiness patterns, over-mocking, and fragile assertions by applying a consistent checklist to unit, integration, and end-to-end tests, identifying conditional logic and AI-generated test anti-patterns before merge.

What is the best way to check pull request test coverage for database and authorization seams?

Test coverage and seam analysis verifies boundaries, database interactions, and authorization coverage to ensure tests prove intended behavior, enforcing state verification and doctrine-based coverage for security and data integrity.

How do I classify flaky test failures during a pull request review?

Flaky test classification applies smell detection and seam discipline to isolate brittle assertions and conditional test logic, categorizing flakiness risks within unit, integration, workflow, and end-to-end test changes.

Does this test review approach work with pytest and LLM eval changes?

Yes, test review applies to pytest changes and LLM eval tests by checking naming, structure, and readability, flagging AI-generated test anti-patterns and verifying state verification across all test layers.

How do I detect AI-generated test anti-patterns and weak assertions?

AI-generated test checks identify anti-patterns and weak assertions by reviewing test structure and enforcing state verification, flagging over-mocking and conditional test logic that fail to prove intended behavior.

When should I run a test audit for regressions and brittle assertions?

Run a test audit during pull request reviews or flaky failure investigations to catch regressions and brittle assertions, enforcing coverage for security, observability, and data integrity across all test changes.