Apply test quality principles when generating or reviewing test code. Enforces Arrange-Act-Assert structure, one behavior per test, assertion quality, test isolation, meaningful naming, and test data management. Use when writing tests, reviewing test code, or when the user mentions 'write tests', 'test this', 'test quality', 'test review', 'improve tests', or 'test structure'. This skill governs the craft of writing individual test cases -- not what to test (that is driven by the code being implemented) but how to write tests that are reliable, readable, and maintainable.
npx skills add https://github.com/techygarg/lattice --skill test-quality
Skill support project custom. Order:
.lattice/config.yaml in repo rootpaths.test_quality for custom doc pathmode:mode: override (or no mode): Custom doc take full control. Use instead default. Must be complete -- is only reference.mode: overlay: Read ./references/defaults.md first, then apply custom doc on top. Custom sections replace match sections (by heading). New sections add after../references/defaults.mdpaths.language_idioms exist in config, read "Testing Patterns" section and adapt §5 (Test Naming), §4 (Test Isolation), §6 (Test Data Builders) to language test framework idioms. Language idioms take precedence over pseudocode defaults.STOP after gen each test. Check ALL before continue. If fail, fix. If ambiguous (see Ambiguity Signals), flag -- show options & reasoning.
Project-specific checks: if loaded doc contains a validation checklist section, apply those after base checklist.
After checklist, scan these. If find, fix before present.
Fire when: (1) completing all tests for a class — new or existing. (2) adding or editing any test in an existing class.
STOP before present. Per-test checks verify individual quality. This review verifies the test suite covers the class contract.
deleteUser has 1 test (happy path only) — does it have error cases?"Run steps 3–5 only, scoped to the changed test:
Multiple valid outcomes. Present options, not choose silent.
Treat test files like production classes:
Refactoring opportunities to surface proactively:
See ./references/defaults.md for AAA structure examples, assertion patterns, isolation techniques, naming conventions, test data builder patterns, and pyramid distribution guidance.
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 techygarg/test-quality 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.