These patterns are intentional and correct - do not report as issues:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpython-code-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches python-code-review from existential-birds/beagle and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate python-code-review. Access via /python-code-review in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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| 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 |
snake_case for functions/variables, CamelCase for classes, UPPER_CASE for constantsAny unless necessary (with comment explaining why)T | None syntax (Python 3.10+)time.sleep, requests) in async functionsawait on all coroutinesexcept: clausesraise ... from to preserve stack traceslogger not print() for output.format() or %These patterns are intentional and correct - do not report as issues:
Any when interacting with untyped libraries - Required when external libraries lack type stubs__init__.py files - Valid for package structure, no code requirednoqa comments - Valid when linter rule doesn't apply to specific casecast() after runtime type check - Correct pattern to inform type checker of narrowed typeOnly 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 |
async def functions → async-patterns.mdLoad and follow review-verification-protocol before reporting any issue.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
wispbit-ai/skills
awesome-skills/code-review-skill
mindrally/skills
shubhamsaboo/awesome-llm-apps
asyrafhussin/agent-skills
shadcn/improve
Solid pick for teams standardizing on skills: python-code-review is focused, and the summary matches what you get after install.
We added python-code-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in python-code-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
python-code-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
python-code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
python-code-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
python-code-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
python-code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: python-code-review is focused, and the summary matches what you get after install.
python-code-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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