Review generated or changed test code against universal testing rules before it ships. Best used reactively after an agent writes, edits, generates, or refactors tests, before presenting, committing, or merging them. Use for pytest (test_*.py, *_test.py), PHPUnit/Pest (*Test.php), Jest/Vitest (*.test.ts, *.spec.js), Go (*_test.go), files under tests/, __tests__/, or spec/, and review requests like 'write tests for X', 'add tests', 'test this', 'review these tests', or PR diffs containing tests. Can also guide test writing when explicitly invoked before the work. This skill is the quality gate that prevents AI-generated test bloat. DO NOT USE for production or implementation code review (use clean-code-guard), CI or test-runner configuration, running or debugging tests, or general architecture discussion.
npx skills add https://github.com/amElnagdy/guard-skills --skill test-guard
You are reviewing generated or changed test code before it ships. Enforce the rules below after the first test-writing pass and before the tests are presented, committed, or merged. Be a sharp reviewer, not a pedantic one: flag what wastes maintenance effort or hides real bugs, ignore cosmetic preferences.
These rules exist because coding agents over-generate tests. The common failure modes: mock-heavy unit tests that assert implementation details, near-duplicate test bodies that differ by one value, and tests that re-verify the framework instead of the project's logic. Each looks productive in a diff and costs maintenance forever.
These rules are universal, but their application is not. Before reviewing:
When writing new tests, ask for each test: "What specific bug does this catch that no other test in this suite catches?" If you can't answer clearly, don't write it.
Test what code does from the caller's perspective. Assert return values and observable side effects. Never assert that an internal helper was called with specific arguments — that test breaks on every refactor while catching nothing.
Violation pattern: asserting a mock of an internal function was called, where that function is not a system boundary.
Fix: assert the return value or the state change the caller observes.
Mock only at system boundaries: network and HTTP calls, LLM APIs, databases, filesystem I/O on external files, clock and randomness, third-party SDKs. Never mock internal classes or helper functions to isolate a "unit" — the seams you create hide the integration bugs worth catching.
When you mock a boundary, assert what the caller *does with the response*, not that the mock received specific arguments.
If two or more tests share identical setup and differ only in input/output values, merge them into one data-driven test (@pytest.mark.parametrize, PHPUnit #[DataProvider], Jest test.each).
When separate tests ARE correct: different setup, different assertions, different mock configurations, or genuinely different scenarios that happen to exercise the same function.
Ask: "What bug does this catch that no other test catches?" Delete tests that only catch typos, verify default values of data classes, or test trivial pass-through logic.
Common unjustified tests: constructors setting attributes, a function rejecting input the type system already forbids, string formatting of log messages, a constant equaling its literal value.
Pattern: test_<scenario>_<expected_outcome>. The name should read like a requirement, not echo the function signature.
| Bad | Good |
|-----|------|
| test_parse_response_missing_field | test_malformed_response_falls_back_to_default |
| test_get_language_no_class | test_element_without_class_returns_empty_language |
| test_add_tags_single_string | test_single_tag_normalizes_to_list |
Tests that reproduce a real production bug are always justified. Reference the incident (date, issue ID, or short description) in the name or a comment, and never delete them. They are exempt from Rule 4 — their justification is the incident.
Don't test that the validation library validates, the ORM commits, the router returns 404, or the test framework's fixtures work. Test *your* logic that sits on top of the framework.
Violation pattern: a test that would still pass if you deleted all the project's custom code and kept only framework defaults.
Never mock a data model, DTO, entity, or state object. Construct a real instance. Mocking state hides field-name typos and validation errors — exactly the bugs worth catching. If constructing the real object is painful, that is design feedback, not a reason to mock; add a small builder or factory helper.
When database queries, schema behavior, or persistence logic *is the subject* of the test, run against a real test database with real migrations applied via fixtures. Mocking the session there tests nothing. Mocking the database is fine when persistence is only a side effect of the behavior under test.
When flagging violations, use this format:
**Rule N violation** in `tests/path/file.ext::<test_name>`
- What: <one sentence describing the violation>
- Fix: <one sentence describing what to do instead>
Group violations by file. If a file has no violations, don't mention it.
Not all violations are equal. Use judgment:
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take amelnagdy/test-guard from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.