Python testing with pytest: fixtures to property-based tests
Write pytest tests with fixtures, parametrization, mocking, coverage, async support, and hypothesis property tests
- What
- Write pytest tests with fixtures, parametrization, mocking, coverage, async support, and hypothesis property tests
- Cost
- Free
- Needs
- Python 3 and pytest installed (pip install pytest; add pytest-cov, pytest-asyncio, pytest-xdist, hypothesis as needed); no accounts or keys required
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @affaan-m's comprehensive pytest playbook for agents writing or improving Python tests: test discovery conventions, fixtures with all four scopes (function, class, module, session) and fixture dependencies, `@pytest.mark.parametrize` patterns with custom ids, test markers for categorization and selective runs, mocking via `unittest.mock` and pytest-mock, coverage analysis (term, HTML, branch, fail-under thresholds), async testing with pytest-asyncio, and property-based testing with hypothesis — plus a recommended test-directory layout with conftest.py, custom assertion helpers, test data factories, exception testing with pytest.raises, and a best-practices checklist (Arrange-Act-Assert, independent tests, edge cases, fast isolated tests). Honest caveats: patterns target the current pytest ecosystem — check plugin names (pytest-mock, pytest-asyncio, pytest-xdist, hypothesis) against your project before installing; MIT-licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: Python 3 and pytest installed (pip install pytest; add pytest-cov, pytest-asyncio, pytest-xdist, hypothesis as needed); no accounts or keys required Install "Python testing with pytest: fixtures to property-based tests" for me. A comprehensive pytest guide for agents: fixtures and scopes, parametrization, markers, mocking (unittest.mock and pytest-mock), coverage, async testing, property-based tests with hypothesis, test layout with conftest.py, and a best-practices checklist — by @affaan-m, MIT-licensed. Repository: https://github.com/affaan-m/ecc/blob/main/.kiro/skills/python-testing/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code, credentials only via the secure vault, allowed hosts declared in the SKILL.md. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "python-testing". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. install pytest and any needed plugins in my project). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.