Generate comprehensive pytest tests - use when generating tests, creating test suites, or testing Python code
npx skills add https://github.com/github/copilot-cli-for-beginners --skill pytest-gen
When generating tests, follow this structure.
@pytest.mark.parametrize for multiple inputsimport pytest
from module_under_test import function_to_test
@pytest.fixture
def sample_data():
"""Provide shared test data."""
return {"key": "value"}
class TestFunctionName:
"""Tests for function_name."""
def test_happy_path(self, sample_data):
result = function_to_test(valid_input)
assert result == expected_output
def test_empty_input(self):
result = function_to_test("")
assert result == expected_for_empty
@pytest.mark.parametrize("input_val,expected", [
("valid", True),
("", False),
(None, False),
])
def test_various_inputs(self, input_val, expected):
assert function_to_test(input_val) == expected
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.
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Use when implementing any feature or bugfix, before writing implementation code
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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 github/pytest-gen 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.