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smithery.ai

prnd-layered-python-code-reviewer

Review Python code changes with a focus on layered architecture boundaries, DI/DIP, configuration validation, typing quality (mypy/flake8 readiness), naming/docstring accuracy, and testability.

First seen Apr 16, 2026

Installation

$ npx skills add https://smithery.ai
View on skills.sh ↗ Website ↗

Summary

  • Review Python code changes with a focus on layered architecture boundaries, DI/DIP, configuration validation, typing quality (mypy/flake8 readiness), naming/docstring accuracy, and testability.
  • Use when asked to “code review”, “PR review”, or “리뷰해줘” to prevent repeat feedback on architecture regressions and low-signal changes.

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Also in this package

Other skills from smithery.ai · top by installs.

npx skills add https://smithery.ai
smithery-ai-cli 7.8K frontend-design 6.2K skill-creator 2.5K ck:ui-ux-pro-max 1.3K pdf 1.2K xlsx 966 pptx 894 docx 884 webapp-testing 1 web-design-reviewer 657

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,607 B
  • docs SUMMARY.md 389 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Layered Python Code Reviewer

Overview

Provide a reusable code review workflow and checklist that applies across Python projects, especially ones that aim for layered architecture and dependency inversion. Produce actionable, concrete review comments (not vague style opinions).

Review Workflow

  1. Identify the change scope

- Summarize touched modules and layers (example layers: domain/, service/, dataset/, adapters/, infra/). - Identify which changes are: (a) core policy/logic, (b) boundary I/O, (c) data shaping, (d) test-only.

  1. Apply hard blockers first

- Dependency direction (DIP): low-level modules must not import high-level policy/service types. - Public interfaces: avoid leaking raw dict across layers; use TypedDict, dataclasses, or Pydantic models. - Config correctness: validate invariants on load (range continuity, required keys, value bounds). - Type/lint hygiene: fix None handling, unused variables, and any Any propagation caused by missing types.

  1. Apply design-quality checks

- Composition root: wiring (ENV/config/I/O/client construction) stays at the boundary (create()/entrypoint). - Separation of concerns: “build payload/message” vs “send request”, “decide” vs “orchestrate”. - Test seams: time/randomness/I/O are injectable; core logic is deterministic.

  1. Write review output

- Group comments by: Architecture, DIP/DI, Typing, Config, Error handling, Tests, Naming/Docs. - For each issue: state impact + preferred pattern + minimal change suggestion.

References

Load and apply:

  • references/principles.md for a project-agnostic checklist and comment templates.
  • references/review-themes.md for common “review-driven refactor” patterns (kept generic on purpose).

Optional Automation

If your project uses clear “layer folders” under a package root, run:

python scripts/checklayeredimports.py --root <packagerootdir> --package-name <toplevelpackage>

Use flags to match your project layout:

  • --layers domain,service,adapters,infra
  • --forbid domain:service,adapters,infra
  • --forbid adapters:service