python-code-quality▌
laurigates/claude-plugins · updated Apr 8, 2026
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Quick reference for Python code quality tools: ruff (linting & formatting), ty (type checking).
Python Code Quality
Quick reference for Python code quality tools: ruff (linting & formatting), ty (type checking).
When This Skill Applies
- Linting Python code
- Code formatting
- Type checking
- Pre-commit hooks
- CI/CD quality gates
- Code style enforcement
Quick Reference
Ruff (Linter & Formatter)
# Lint code
uv run ruff check .
# Auto-fix issues
uv run ruff check --fix .
# Format code
uv run ruff format .
# Check and format
uv run ruff check --fix . && uv run ruff format .
# Show specific rule
uv run ruff check --select E501 # Line too long
# Ignore specific rule
uv run ruff check --ignore E501
ty (Type Checking)
# Type check project
uv run ty check
# Type check specific file
uv run ty check src/module.py
# Check with explicit Python version
uv run ty check --python 3.11
# Verbose output
uv run ty check --verbose
pyproject.toml Configuration
[tool.ruff]
line-length = 88
target-version = "py311"
[tool.ruff.lint]
select = [
"E", # pycodestyle errors
"W", # pycodestyle warnings
"F", # pyflakes
"I", # isort
"N", # pep8-naming
"UP", # pyupgrade
"B", # flake8-bugbear
]
ignore = [
"E501", # line too long (handled by formatter)
]
[tool.ruff.lint.isort]
known-first-party = ["myproject"]
[tool.ty]
python-version = "3.11"
exclude = [
"**/__pycache__",
"**/.venv",
"tests",
]
[tool.ty.rules]
possibly-unbound = "warn"
Type Hints
# Modern type hints (Python 3.10+)
def process_data(
items: list[str], # Not List[str]
config: dict[str, int], # Not Dict[str, int]
optional: str | None = None, # Not Optional[str]
) -> tuple[bool, str]: # Not Tuple[bool, str]
return True, "success"
# Type aliases
from typing import TypeAlias
UserId: TypeAlias = int
UserDict: TypeAlias = dict[str, str | int]
def get_user(user_id: UserId) -> UserDict:
return {"id": user_id, "name": "Alice"}
Pre-commit Configuration
# .pre-commit-config.yaml
repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.1.9
hooks:
- id: ruff
args: [--fix]
- id: ruff-format
- repo: https://github.com/astral-sh/ty
rev: v0.0.10
hooks:
- id: ty
Common Ruff Rules
- E501: Line too long
- F401: Unused import
- F841: Unused variable
- I001: Import not sorted
- N806: Variable should be lowercase
- B008: Function call in argument defaults
See Also
python-testing- Testing code qualityuv-project-management- Adding quality tools to projectspython-development- Core Python patterns
References
- Ruff: https://docs.astral.sh/ruff/
- ty: https://docs.astral.sh/ty/
- Detailed guide: See REFERENCE.md
How to use python-code-quality on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add python-code-quality
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches python-code-quality from GitHub repository laurigates/claude-plugins and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate python-code-quality. Access the skill through slash commands (e.g., /python-code-quality) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★67 reviews- ★★★★★Advait Thompson· Dec 28, 2024
Useful defaults in python-code-quality — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ganesh Mohane· Dec 24, 2024
python-code-quality fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Zaid Singh· Dec 24, 2024
python-code-quality has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Mia Gonzalez· Dec 20, 2024
We added python-code-quality from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aditi Singh· Dec 20, 2024
Keeps context tight: python-code-quality is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Isabella Liu· Dec 12, 2024
We added python-code-quality from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yusuf Ghosh· Dec 8, 2024
python-code-quality reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hiroshi Jackson· Nov 27, 2024
python-code-quality is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Nia Ghosh· Nov 19, 2024
python-code-quality fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Layla Dixit· Nov 15, 2024
Useful defaults in python-code-quality — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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