基于 SOC 职业分类
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Implementing WCAG accessibility guidelines, semantic HTML5, and screen reader ARIA roles.
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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.
| name | python-unittest |
| description | Writing test cases, assertions, mocking API calls, and running test runner pipelines using unittest. |
| allowed-tools | Read Write Edit Bash |
| license | MIT license |
| metadata | {"skill-author":"Lord1Egypt"} |
unittest is the built-in testing framework in Python, offering clean class-based test configurations.
Use to create unit tests and verify the logic correctness of your code modules.
import unittest
def add_values(x, y):
return x + y
class TestMathOperations(unittest.TestCase):
def test_addition(self):
self.assertEqual(add_values(2, 3), 5)
if __name__ == '__main__':
unittest.main()
Configure test fixtures (setUp/tearDown), mock external requests using unittest.mock.patch, and assert custom errors.