Write pytest tests for Tool Master tools with mocking, fixtures, and async patterns.
npx skills add https://github.com/dangerpotter/tool_master --skill tool-testing
Please help me install this Agent Skill. Skill: tool-testing Source: https://github.com/dangerpotter/tool_master/tree/main/.claude/skills/tool-testing Command: npx skills add https://github.com/dangerpotter/tool_master --skill tool-testing
This Skill helps developers and QA engineers write, maintain, and improve tests for Tool Master tools. It covers mocking, fixtures, and async test patterns, reducing flaky tests and speeding up validation.
Create a basic test module under tests/test_<tool>_tools.py with a simple test for a tool, then run pytest to verify.
High-intent search queries and answers about installing and using this skill.
Write pytest tests by creating a test module under tests/test_<tool>_tools.py with test functions that verify tool metadata and execution results. Use pytest fixtures and mocking to isolate dependencies, then run pytest to validate your test suite.
Pytest fixtures let you mock HTTP calls, environment variables, and external dependencies. Define fixtures that return mock objects, inject them into test functions, and verify tool behavior without calling live services.
Yes. Use pytest.mark.asyncio to write asyncio-friendly tests for asynchronous tools. This decorator enables pytest to handle async test functions and fixtures while maintaining the same mocking and validation patterns.
Tests should verify tool metadata accuracy, validate execution results against expected outputs, confirm proper error handling in both sync and async contexts, and ensure mocks and fixtures function correctly across different tool interactions.
Yes. Pytest fixtures and mocking eliminate external service dependencies that cause flakiness. By controlling inputs and isolating components, you create deterministic tests that run consistently and speed up validation cycles.
Apply pytest-based templates that enforce consistent test structure, add fixtures to cover edge cases, mock failure scenarios, and validate error handling. This systematic approach fills coverage gaps and catches regressions.
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