192 skills · sorted by relevance
Generate pytest-based unit tests for Python code. Creates test files following pytest conventions with proper fixtures, mocking, and parametrization.
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Use when testing Django applications with pytest - TDD workflow, pytest-django setup, factory_boy and model-bakery fixtures, DRF API testing, mocking and patching, integration tests, mutation testing with mutmut, or coverage
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Build Python applications that handle other work while waiting for network or database operations. Use asyncio and async/await to run tasks concurrently, manage errors and timeouts, and handle cancellation with appropriate cleanup.
Install rtk in NanoClaw agent containers to compress development command output and reduce token use. A Claude Code hook automatically routes Bash calls through rtk, with runtime checks to verify installation and a command to inspect savings.
Scan API route definitions and generate test files for authentication, input validation, error responses, pagination, uploads, and rate limits. Supports Next.js App Router, Express, FastAPI, and Django REST, with Node and Python test outputs.
Run TVM's TIRx compiler tests alongside tirx-kernels import and correctness checks. It verifies the local build, selects GPUs, runs parallel tests, saves full logs, and guides failure investigation and result reporting.
Develop and verify changes to Apache Beam's Python SDK. Set up a virtual environment, run tests, build installable packages or containers, and run data processing pipelines with the modified SDK.
Develop and troubleshoot Apache Beam's YAML SDK. Understand its source files, run data processing pipelines locally or on Dataflow, test changes, and investigate provider issues or mismatched record fields.
Writes and runs in-memory tests for GenLayer intelligent contracts. It checks state changes, permissions, and error handling, and uses simulated web and language model responses to test parsing without a server or Docker.
Generate tests for a specified file or module using the project’s testing conventions. Cover expected behavior, unusual inputs, boundaries, failures, and external interactions, with independent tests and mock substitutes for external dependencies.
Review existing tests to see whether their assertions, the checks that verify expected behavior, are meaningful and varied. Get metrics, examples of weak checks, and specific recommendations for improving verification.
Set up a consistent local environment for developing ClawTeam, then validate changes with focused tests and real agent workflows. Includes scripts for connecting other projects to local skills and guidance for investigating coordination bugs.
Generate comprehensive pytest tests - use when generating tests, creating test suites, or testing Python code
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Generate pytest test cases for Python functions and classes
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Python testing patterns with pytest, fixtures, TDD, mocking, async and integration tests. Use when writing or auditing a Python test suite.
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Write effective unit tests with Jest, Vitest, or pytest. Covers the test-doubles taxonomy (stub/spy/mock/fake), Arrange-Act-Assert, coverage threshold configuration and CI gating, snapshot testing, fake timers, and mutation testing with Stryker/mutmut. Use when: "unit test," "Jest," "Vitest," "pytest," "mock," "coverage threshold," "test doubles," "mutation testing," "fake timers," "snapshot test." Not for: interpreting coverage reports or finding coverage gaps — use coverage-analysis; AI generating the test code for you — use ai-test-generation; auditing existing tests for smells — use ai-qa-review; browser/component rendering assertions — use cypress-automation or visual-testing. Related: coverage-analysis, ci-cd-integration, ai-test-generation, shift-left-testing.
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运行 pytest + 结构化分析失败原因。触发：运行测试、跑测试、run test、pytest、失败分析、allure。
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Reviews pytest test code for async patterns, fixtures, parametrize, and mocking. Use when reviewing test_*.py files, checking async test functions, fixture usage, or mock patterns.
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Analyze specific pytest failures using authoritative contracts and a cited product/test evidence chain. Use for failing test names or pytest output, distinguishing product, interface, oracle, and harness defects without automatically changing expectations or forcing an architectural redesign.
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Use when implementing a Python feature, adding CLI commands, writing pytest suites, reviewing Python code, debugging, or refactoring. The primary Python engineering workflow orchestrator — classifies the task and delegates through this plugin's own specialist agents (architect → implement → test → review), sized to the task. Delegates to python-cli-architect (implementation), python-pytest-architect (tests), code-reviewer (review), python-cli-design-spec (architecture). Triggers on any Python task requiring specialist agent coordination or multi-agent execution.
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Add controlled retries to pytest runs so agents can contain flaky tests and report final failures without rerunning whole suites by hand.
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Modern Python tooling best practices using uv, ruff, ty, and pytest. Mandates the Trail of Bits Python coding standards for project setup, dependency management, linting, type checking, and testing. Based on patterns from trailofbits/cookiecutter-python.
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Aims for high test coverage using pytest, testing both common and edge cases.
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Use when testing PyQt/PySide6 applications with pytest-qt - qtbot fixture, signal/wait patterns, mouse/keyboard simulation, dialog testing, model/view testing, threaded code testing, or manual debugging techniques
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