python-best-practices

0xbigboss/claude-code · updated Apr 8, 2026

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$npx skills add https://github.com/0xbigboss/claude-code --skill python-best-practices
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summary

Type-first Python development using dataclasses, discriminated unions, NewType, and Protocol to make illegal states unrepresentable.

  • Define data models and function signatures before implementation; use frozen dataclasses, Literal-based discriminated unions, and NewType for domain primitives to prevent invalid states at type-check time
  • Leverage Protocol for structural typing, TypedDict for external data shapes, and exhaustive pattern matching with match statements to catch incomplete lo
skill.md

Python Best Practices

Follows type-first, functional, and error handling patterns from CLAUDE.md. This skill covers language-specific idioms only.

Make Illegal States Unrepresentable

Use Python's type system to prevent invalid states at type-check time.

Frozen dataclasses for immutable domain models:

from dataclasses import dataclass
from datetime import datetime

@dataclass(frozen=True)
class User:
    id: str
    email: str
    name: str
    created_at: datetime

# Frozen dataclasses are immutable — no accidental mutation

Discriminated unions with Literal:

from dataclasses import dataclass
from typing import Literal

@dataclass
class Success:
    status: Literal["success"] = "success"
    data: str

@dataclass
class Failure:
    status: Literal["error"] = "error"
    error: Exception

RequestState = Success | Failure

def handle_state(state: RequestState) -> None:
    match state:
        case Success(data=data):
            render(data)
        case Failure(error=err):
            show_error(err)

NewType for domain primitives:

from typing import NewType

UserId = NewType("UserId", str)
OrderId = NewType("OrderId", str)

def get_user(user_id: UserId) -> User:
    # Type checker prevents passing OrderId here
    ...

Protocol for structural typing:

from typing import Protocol

class Readable(Protocol):
    def read(self, n: int = -1) -> bytes: ...

def process_input(source: Readable) -> bytes:
    # Accepts any object with a read() method — no inheritance required
    return source.read()

Python-Specific Error Handling

Chain exceptions with from err to preserve the original traceback:

try:
    data = json.loads(raw)
except json.JSONDecodeError as err:
    raise ValueError(f"invalid JSON payload: {err}") from err

Structured Logging

Use a module-level logger with %s formatting (deferred string interpolation):

import logging

logger = logging.getLogger("myapp.widgets")

def create_widget(name: str) -> Widget:
    logger.debug("creating widget: %s", name)
    widget = Widget(name=name)
    logger.debug("created widget id=%s", widget.id)
    return widget

Optional: ty

For fast type checking, consider ty from Astral (creators of ruff and uv). Written in Rust, significantly faster than mypy or pyright.

uvx ty check          # run directly, no install needed
uvx ty check src/     # check specific path
# pyproject.toml
[tool.ty]
python-version = "3.12"

When to choose:

  • ty — fastest, good for CI and large codebases (early stage, rapidly evolving)
  • pyright — most complete type inference, VS Code integration
  • mypy — mature, extensive plugin ecosystem
how to use python-best-practices

How to use python-best-practices on Cursor

AI-first code editor with Composer

1

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-best-practices
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/0xbigboss/claude-code --skill python-best-practices

The skills CLI fetches python-best-practices from GitHub repository 0xbigboss/claude-code and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/python-best-practices

Reload or restart Cursor to activate python-best-practices. Access the skill through slash commands (e.g., /python-best-practices) 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

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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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.644 reviews
  • Nia Okafor· Dec 28, 2024

    We added python-best-practices from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ama Chen· Dec 24, 2024

    Keeps context tight: python-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Luis Desai· Dec 16, 2024

    python-best-practices fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ava Ghosh· Nov 23, 2024

    Registry listing for python-best-practices matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Arya Smith· Nov 19, 2024

    Keeps context tight: python-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kwame Wang· Nov 15, 2024

    We added python-best-practices from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Noor Bhatia· Oct 14, 2024

    python-best-practices fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ren Sharma· Oct 10, 2024

    python-best-practices has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ama Tandon· Oct 6, 2024

    Solid pick for teams standardizing on skills: python-best-practices is focused, and the summary matches what you get after install.

  • Sakshi Patil· Sep 17, 2024

    Keeps context tight: python-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.

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