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Agent Skill · 开发

py-test-quality(py 测试质量)

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

py-test-quality 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 20,462。

总安装 220周安装 9GitHub Stars 27下载 71核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:py-test-quality(py 测试质量)
来源仓库:https://github.com/l-mb/python-refactoring-skills
仓库路径:skills/py-test-quality
安装命令:
npx skills add https://github.com/l-mb/python-refactoring-skills --skill py-test-quality
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/l-mb/python-refactoring-skills --skill py-test-quality

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于辅助测试设计、自动化测试、用例整理和回归验证。py-test-quality 属于开发类 Skill,可作为该场景下的辅助能力补充。适合编写单元测试、端到端测试或根据失败日志定位问题。使用时需确认项目测试框架、运行命令和夹具数据,避免误改逻辑。涉及浏览器或外部服务时,应区分本地模拟与生产环境。

安装前建议核对来源仓库与权限范围,确保操作安全合规。

来源仓库内路径:skills/py-test-quality

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/l-mb/python-refactoring-skills --skill py-test…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

py-test-quality 怎么安装?

在终端执行 npx skills add https://github.com/l-mb/python-refactoring-skills --skill py-test-quality。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

py-test-quality 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

py-test-quality 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

py-test-quality 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

py-test-quality 用的人多吗?

公开数据是累计安装 220 次,近一周安装 9 次,下载 71 次,GitHub 27 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

py-test-quality 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
py-test-quality

py 测试质量 · 本页

22027只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

# 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):

[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

# 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:

[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

# 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

# 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:

# .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)

平台分布

Codex

35.54%
按下载量换算25

Claude

27.96%
按下载量换算20

Cursor

17.9%
按下载量换算13

Gemini CLI

9.1%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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