Py Test Quality logo

Py Test Quality

Community
l-mb
py-test-quality

Measure and improve test coverage and test suite quality using code coverage and mutation testing. Ensures tests actually catch bugs.

Overview

Publisherl-mb
Repositorypython-refactoring-skills
Skill namepy-test-quality
Stars
57
Forks
1
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by l-mb on GitHub. Read the source before you install it.

Installation

Install the Py Test Quality AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/l-mb/python-refactoring-skills/tree/main/skills/py-test-quality
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/l-mb/python-refactoring-skills.git /tmp/python-refactoring-skills
mkdir -p .claude/skills
cp -r /tmp/python-refactoring-skills/skills/py-test-quality .claude/skills/py-test-quality
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Py Test Quality in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Py Test Quality on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Py Test Quality is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Python Test Quality Analysis

Measure test coverage and verify test suite effectiveness using coverage analysis and mutation testing.

Objectives

  1. Measure code coverage comprehensively
  2. Identify untested code paths
  3. Verify test suite catches bugs (mutation testing)
  4. Enable safe refactoring through high test coverage
  5. Track coverage trends over time

Required Tools

Add to [dependency-groups] dev: "pytest", "pytest-cov", "mutmut", "coverage" Optional: "cosmic-ray" (advanced mutation testing)

  • pytest-cov: Code coverage measurement
  • mutmut: Mutation testing - verifies tests catch bugs
  • cosmic-ray: Advanced mutation testing (slower)

Permissions: Run py-quality-setup first to configure .claude/settings.local.json with all needed tool permissions.

Coverage Analysis

Measure Coverage

bash
# Run tests with coverage
pytest --cov=. --cov-report=term-missing
pytest --cov=. --cov-report=html  # Generate HTML report
pytest --cov=. --cov-report=term --cov-report=html  # Both

# Coverage with specific targets
pytest --cov=src --cov=lib tests/
pytest --cov=mypackage --cov-branch  # Include branch coverage

# Fail if coverage below threshold
pytest --cov=. --cov-fail-under=80

# Show only uncovered lines
pytest --cov=. --cov-report=term-missing:skip-covered

Configure Coverage

Add to pyproject.toml (see py-quality-setup for base configuration):

toml
[tool.coverage.run]
source = ["src"]  # Adjust to your source directory
omit = [
    "*/tests/*",
    "*/test_*.py",
    "*/__pycache__/*",
    "*/venv/*",
    "*/.venv/*",
]
branch = true  # Enable branch coverage

[tool.coverage.report]
precision = 2
show_missing = true
skip_covered = false
exclude_lines = [
    "pragma: no cover",
    "def __repr__",
    "raise AssertionError",
    "raise NotImplementedError",
    "if __name__ == .__main__.:",
    "if TYPE_CHECKING:",
    "@abstractmethod",
]

[tool.coverage.html]
directory = "htmlcov"

Interpret Coverage Reports

Name                 Stmts   Miss Branch BrPart  Cover   Missing
----------------------------------------------------------------
src/auth.py            45      5     12      2    87%   23-25, 67
src/database.py        89      0     18      0   100%
src/handlers.py       123     35     28     12    68%   45-78, 99-110
----------------------------------------------------------------
TOTAL                 257     40     58     14    82%

Key metrics:

  • Stmts: Total statements
  • Miss: Uncovered statements
  • Branch: Total branches (if/else, etc.)
  • BrPart: Partially covered branches (one path tested, not both)
  • Cover: Coverage percentage
  • Missing: Line numbers not covered

Coverage targets:

  • ≥80%: Minimum acceptable
  • ≥90%: Good coverage
  • 100%: Ideal (may not be practical for all code)

Mutation Testing

Mutation testing verifies your tests actually catch bugs by introducing small changes (mutations) and checking if tests fail.

Run Mutation Testing

bash
# Using mutmut (easier, faster)
mutmut run                    # Run all mutations
mutmut run --paths-to-mutate=src/  # Specific directory
mutmut results                # Show summary
mutmut show <mutation-id>     # Show specific mutation
mutmut apply <mutation-id>    # Apply mutation to see code change

# Common workflow
mutmut run
mutmut results               # Shows: survived, killed, timeout
mutmut show 1                # Examine first surviving mutation

Configure Mutmut

Add to setup.cfg or pyproject.toml:

toml
[tool.mutmut]
paths_to_mutate = "src/"
backup = false
runner = "pytest -x --tb=short"
tests_dir = "tests/"

Interpret Mutation Results

Legend for output:
🎉 Killed mutants: Tests caught the bug (good!)
⏰ Timeout: Mutation created infinite loop (acceptable)
🤔 Suspicious: Needs investigation
🙁 Survived: Bug not caught by tests (bad!)

Mutation score: killed / (killed + survived) * 100%

Target mutation scores:

  • ≥75%: Good test quality
  • ≥85%: Excellent test quality
  • 100%: Perfect (rarely achievable)

Address Surviving Mutations

bash
# 1. Identify surviving mutations
mutmut results

# 2. Show specific mutation
mutmut show 5

# Example output:
# src/auth.py:23
# -    if user.age >= 18:
# +    if user.age > 18:

# 3. Write test to kill this mutation
def test_user_exactly_18_is_adult():
    user = User(age=18)
    assert is_adult(user) is True  # This would fail with > instead of >=

# 4. Re-run mutmut
mutmut run

# 5. Verify mutation now killed
mutmut results

Coverage-Guided Refactoring

Golden rule: Only refactor well-tested code. If coverage is low, write tests first.

Workflow

1. Run: pytest --cov=. --cov-report=html --cov-fail-under=80
2. If coverage < 80%:
   a. Open htmlcov/index.html
   b. Identify modules with low coverage (red/yellow)
   c. Write tests to increase coverage to ≥80%
   d. Re-run coverage to verify
3. Run: mutmut run (optional but recommended)
4. If mutation score < 75%:
   a. Review surviving mutations
   b. Write tests to kill mutations
   c. Re-run mutmut
5. NOW safe to refactor:
   a. Apply refactoring patterns
   b. Re-run: pytest --cov=. (ensure coverage maintained)
   c. Re-run: mutmut run (ensure mutation score maintained)

Integration with Refactoring

Before Refactoring Checklist

bash
# 1. Measure baseline coverage
pytest --cov=src/module_to_refactor.py --cov-report=term-missing

# 2. If coverage < 80%, write tests first
# ... write tests ...

# 3. Verify tests are effective
mutmut run --paths-to-mutate=src/module_to_refactor.py

# 4. Now proceed with refactoring
# ... refactor ...

# 5. Verify coverage maintained
pytest --cov=src/module_to_refactor.py --cov-fail-under=80

# 6. Verify tests still effective
mutmut run --paths-to-mutate=src/module_to_refactor.py

CI/CD Integration

Add coverage enforcement to CI:

yaml
# .github/workflows/test.yml
- name: Run tests with coverage
  run: |
    pytest --cov=. --cov-report=term --cov-report=xml --cov-fail-under=80

- name: Upload coverage to Codecov
  uses: codecov/codecov-action@v3
  with:
    file: ./coverage.xml

Verification Checklist

  • pytest --cov=. --cov-fail-under=80 passes
  • Coverage report reviewed (htmlcov/index.html)
  • Critical paths have test coverage
  • Mutation testing run on critical modules (mutmut run)
  • Mutation score ≥75% for critical code
  • Coverage configuration in pyproject.toml

Examples

Example: Increase coverage before refactoring

1. Run: pytest --cov=src/handlers.py --cov-report=html
2. Coverage: 55% (too low to refactor safely)
3. Open htmlcov/handlers_py.html
4. Lines 45-78 not covered (error handling paths)
5. Write tests for error cases:
   - test_handler_invalid_input()
   - test_handler_database_error()
   - test_handler_missing_params()
6. Re-run: pytest --cov=src/handlers.py
7. Coverage now: 85% (safe to refactor)
8. Proceed with complexity reduction refactoring
9. After refactoring: pytest --cov=src/handlers.py --cov-fail-under=85
10. Verify coverage maintained at 85%

Example: Use mutation testing to improve tests

1. Run: mutmut run --paths-to-mutate=src/auth.py
2. Results: 15 killed, 3 survived (83% mutation score)
3. Run: mutmut show 5
4. Mutation: Changed >= to > in age check
5. Realize: Missing boundary test for age exactly 18
6. Write: test_user_exactly_18_is_adult()
7. Run: mutmut run --paths-to-mutate=src/auth.py
8. Results: 16 killed, 2 survived (89% mutation score)
9. Repeat for remaining survivors
10. Final: 18 killed, 0 survived (100% mutation score)

Example: Coverage-guided refactoring session

1. Target: Refactor src/payment.py (complexity D, 150 lines)
2. Check coverage: pytest --cov=src/payment.py --cov-report=term-missing
3. Coverage: 92% (good! safe to refactor)
4. Check test quality: mutmut run --paths-to-mutate=src/payment.py
5. Mutation score: 78% (acceptable)
6. Proceed with refactoring:
   - Extract 4 smaller functions
   - Reduce complexity from D to A/B
7. After refactoring:
   - pytest --cov=src/payment.py --cov-fail-under=92 ✓
   - mutmut run --paths-to-mutate=src/payment.py
   - Mutation score: 80% (improved!)
8. Commit changes with confidence

Example: Set up coverage tracking in CI

1. Add to pyproject.toml:
   [tool.coverage.run]
   source = ["src"]
   branch = true

2. Create .github/workflows/test.yml:
   - pytest --cov=. --cov-fail-under=80

3. First run fails: Coverage 67%
4. Write tests to reach 80%
5. CI now passes
6. All future PRs must maintain 80% coverage
7. Consider increasing threshold as coverage improves

Related Skills

  • Prerequisites: py-quality-setup (configure pytest-cov in pyproject.toml)
  • Enables: py-security, py-code-health, py-complexity (safe refactoring with test coverage)
  • Enforcement: py-git-hooks (add coverage checks to CI)

Frequently asked questions

What does the Py Test Quality AI skill do?

Measure and improve test coverage and test suite quality using code coverage and mutation testing. Ensures tests actually catch bugs.

Why use Py Test Quality on TypingMind?

Because you install it once and use it with any model. Py Test Quality is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Py Test Quality in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/l-mb/python-refactoring-skills/tree/main/skills/py-test-quality. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Py Test Quality?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Py Test Quality?

As many as you like. As long as a model supports skills, you can use Py Test Quality with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Py Test Quality AI skill free?

Yes. It is published on GitHub by l-mb under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇