python-code-review▌
existential-birds/beagle · updated Apr 8, 2026
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These patterns are intentional and correct - do not report as issues:
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_casefor functions/variables,CamelCasefor classes,UPPER_CASEfor constants - Inline comments separated by at least two spaces
Type Safety
- Type hints on all function parameters and return types
- No
Anyunless necessary (with comment explaining why) - Proper
T | Nonesyntax (Python 3.10+)
Async Patterns
- No blocking calls (
time.sleep,requests) in async functions - Proper
awaiton all coroutines
Error Handling
- No bare
except:clauses - Specific exception types with context
-
raise ... fromto preserve stack traces
Common Mistakes
- No mutable default arguments
- Using
loggernotprint()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
Anywhen interacting with untyped libraries - Required when external libraries lack type stubs - Empty
__init__.pyfiles - Valid for package structure, no code required noqacomments - 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 deffunctions → async-patterns.md - Reviewing try/except blocks → error-handling.md
- General Python review → common-mistakes.md
Review Questions
- Does the code follow PEP8 formatting (indentation, line length, whitespace)?
- Are imports properly grouped (stdlib → third-party → local)?
- Do names follow conventions (snake_case, CamelCase, UPPER_CASE)?
- Are all function signatures fully typed?
- Are async functions truly non-blocking?
- Do exceptions include meaningful context?
- 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 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-review
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches python-code-review from GitHub repository existential-birds/beagle 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-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
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
4.5★★★★★37 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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