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Review test code for quality, design, and completeness after implementing a feature or fixing a bug.
Passed 21 of 22 test cases with the skill, and 17 of 22 without it.
and 1 more in the full report
4 fixed17 passed either way1 still failing0 broke1 errored
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are using an automated agent to post code review feedback directly onto a GitHub Pull Request for UserAccountServiceTest.java. What attribution message should be included at the end of the review comment when posting to a PR?
| Checked | Without skill | With skill |
|---|---|---|
| Output includes attribution referencing the review-testing skill. | Missed | Passed |
The instructions your model reads when the skill loads.
You are reviewing test code written alongside a feature implementation or bug fix. Ensure the tests are well-designed, thorough, and maintainable — not just that they pass. Tests that merely mirror implementation details create false confidence and become a maintenance burden during refactoring.
Identify what to review:
Read test files first, before production code. If you can infer the feature's requirements and edge cases from the tests alone, that's a sign the tests are well-written. If you need to read the implementation to understand what the tests are doing, that's a finding worth reporting.
Weigh each test against four qualities:
Regression protection — Does this test actually catch bugs? A test that exercises trivial code or skips complex branches protects against nothing. Check: does the test touch business-critical logic, or only verify the happy path of a simple getter?
Refactoring resilience — Will this test break when someone restructures code without changing behavior? Tests coupled to internal method names, call sequences, or private state punish every cleanup with false failures, eroding trust in the suite.
Fast feedback — Unit tests should run in milliseconds. If a test hits the filesystem, network, or database unnecessarily, that's a design issue. But don't confuse speed with value — an integration test verifying a real database query is better than a fast unit test that mocks everything and verifies nothing.
Maintainability — Can someone unfamiliar with this code read the test and understand what it verifies and why? Tests with sprawling setup, cryptic names, or deeply nested mocking fail this check.
The most common weakness in generated tests: asserting only the obvious output and missing the full "blast radius" of a state change.
When a test triggers an action, ask what else changed. If a test adds an item to a cart, does it only check the item count? Or does it also verify the price calculation, subtotal update, and that other items are unaffected?
Flag when:
test cases passed, vs 17 / 22 without
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