python-code-review

existential-birds/beagle · updated Apr 8, 2026

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$npx skills add https://github.com/existential-birds/beagle --skill python-code-review
0 commentsdiscussion
summary

These patterns are intentional and correct - do not report as issues:

skill.md

Python Code Review

Quick Reference

Issue Type Reference
Indentation, line length, whitespace, naming references/pep8-style.md
Missing/wrong type hints, Any usage references/type-safety.md
Blocking calls in async, missing await references/async-patterns.md
Bare except, missing context, logging references/error-handling.md
Mutable defaults, print statements references/common-mistakes.md

Review Checklist

PEP8 Style

  • 4-space indentation (no tabs)
  • Line length ≤79 characters (≤72 for docstrings/comments)
  • Two blank lines around top-level definitions, one within classes
  • Imports grouped: stdlib → third-party → local (blank line between groups)
  • No whitespace inside brackets or before colons/commas
  • Naming: snake_case for functions/variables, CamelCase for classes, UPPER_CASE for constants
  • Inline comments separated by at least two spaces

Type Safety

  • Type hints on all function parameters and return types
  • No Any unless necessary (with comment explaining why)
  • Proper T | None syntax (Python 3.10+)

Async Patterns

  • No blocking calls (time.sleep, requests) in async functions
  • Proper await on all coroutines

Error Handling

  • No bare except: clauses
  • Specific exception types with context
  • raise ... from to preserve stack traces

Common Mistakes

  • No mutable default arguments
  • Using logger not print() for output
  • f-strings preferred over .format() or %

Valid Patterns (Do NOT Flag)

These patterns are intentional and correct - do not report as issues:

  • Type annotation vs type assertion - Annotations declare types but are not runtime assertions; don't confuse with missing validation
  • Using Any when interacting with untyped libraries - Required when external libraries lack type stubs
  • Empty __init__.py files - Valid for package structure, no code required
  • noqa comments - Valid when linter rule doesn't apply to specific case
  • Using cast() after runtime type check - Correct pattern to inform type checker of narrowed type

Context-Sensitive Rules

Only flag these issues when the specific conditions apply:

Issue Flag ONLY IF
Generic exception handling Specific exception types are available and meaningful
Unused variables Variable lacks _ prefix AND isn't used in f-strings, logging, or debugging

When to Load References

  • Reviewing code formatting/style → pep8-style.md
  • Reviewing function signatures → type-safety.md
  • Reviewing async def functions → async-patterns.md
  • Reviewing try/except blocks → error-handling.md
  • General Python review → common-mistakes.md

Review Questions

  1. Does the code follow PEP8 formatting (indentation, line length, whitespace)?
  2. Are imports properly grouped (stdlib → third-party → local)?
  3. Do names follow conventions (snake_case, CamelCase, UPPER_CASE)?
  4. Are all function signatures fully typed?
  5. Are async functions truly non-blocking?
  6. Do exceptions include meaningful context?
  7. Are there any mutable default arguments?

Before Submitting Findings

Load and follow review-verification-protocol before reporting any issue.

how to use python-code-review

How to use python-code-review 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-code-review
2

Execute installation command

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

$npx skills add https://github.com/existential-birds/beagle --skill python-code-review

The skills CLI fetches python-code-review from GitHub repository existential-birds/beagle 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-code-review

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

GET_STARTED →

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

4.537 reviews
  • Min Johnson· Dec 20, 2024

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

  • Ganesh Mohane· Dec 12, 2024

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

  • Kabir Okafor· Dec 8, 2024

    Useful defaults in python-code-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Xiao Robinson· Nov 11, 2024

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

  • Rahul Santra· Nov 3, 2024

    python-code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Pratham Ware· Oct 22, 2024

    python-code-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Aanya Smith· Oct 2, 2024

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

  • Olivia Agarwal· Sep 9, 2024

    python-code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Piyush G· Sep 1, 2024

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

  • Aarav Thomas· Sep 1, 2024

    python-code-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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