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review-python审查 Python

Agent Skill

用于辅助 Python 项目开发、测试、依赖管理和常见框架工作流。它适合让 Agent 阅读 Python 代码、定位测试问题、整理运行命令、生成脚本或分析数据处理逻辑。使用时需要确认项目虚拟环境、依赖版本和测试入口;涉及执行脚本、读写文件、访问数据库或调用外部 API 时,应先明确运行目录和输入输出范围,避免误改生产数据。

总安装

456

周安装

19

GitHub Stars

54

下载量

152
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:review-python(审查 Python)
来源仓库:https://github.com/existential-birds/beagle
仓库路径:skills/review-python
安装命令:
npx skills add https://github.com/existential-birds/beagle --skill review-python
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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

简介

review-python 用于 Python 后端代码审查,集成 ruff 和 mypy 工具链。

  • 检查类型注解、异常处理和数据库查询安全性。
  • 验证 pytest 测试用例与 fixtures 数据隔离性。
  • 需先运行项目本地检查器再报告问题,避免误报。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Backend Code Review

Arguments

  • --parallel: Spawn specialized subagents per technology area
  • Path: Target directory (default: current working directory)

Step 1: Identify Changed Files

git diff --name-only $(git merge-base HEAD main)..HEAD | grep -E '\.py$'

Step 2: Verify Linter Status

CRITICAL: Run project linters BEFORE flagging any style or type issues.

# Check if ruff config exists and run it
if [ -f "pyproject.toml" ] || [ -f "ruff.toml" ]; then
    ruff check <changed_files>
fi

# Check if mypy config exists and run it
if [ -f "pyproject.toml" ] || [ -f "mypy.ini" ]; then
    mypy <changed_files>
fi

Rules:

  • If a linter passes for a specific rule (e.g., line length), DO NOT flag that issue manually
  • Linter configuration is authoritative for style rules
  • Only flag issues that linters cannot detect (semantic issues, architectural problems)

Why: Analysis of 24 review outcomes showed 4 false positives (17%) where reviewers flagged line-length violations that ruff check confirmed don't exist. The linter's configuration reflects intentional project decisions.

Step 3: Detect Technologies

# Detect Pydantic-AI
grep -r "pydantic_ai\|@agent\.tool\|RunContext" --include="*.py" -l | head -3

# Detect SQLAlchemy
grep -r "from sqlalchemy\|Session\|relationship" --include="*.py" -l | head -3

# Detect Postgres-specific
grep -r "psycopg\|asyncpg\|JSONB\|GIN" --include="*.py" -l | head -3

# Check for test files
git diff --name-only $(git merge-base HEAD main)..HEAD | grep -E 'test.*\.py$'

Step 4: Load Verification Protocol

Load beagle-python:review-verification-protocol skill and keep its checklist in mind throughout the review.

Step 5: Load Skills

Use the Skill tool to load each applicable skill (e.g., Skill(skill: "beagle-python:python-code-review")).

Always load:

  • beagle-python:python-code-review
  • beagle-python:fastapi-code-review

Conditionally load based on detection:

ConditionSkill
Test files changedbeagle-python:pytest-code-review
Pydantic-AI detectedbeagle-ai:pydantic-ai-common-pitfalls
SQLAlchemy detectedbeagle-python:sqlalchemy-code-review
Postgres detectedbeagle-python:postgres-code-review

Step 6: Review

Sequential (default):

  1. Load applicable skills
  2. Review Python quality issues first
  3. Review FastAPI patterns
  4. Review detected technology areas
  5. Consolidate findings

Parallel (--parallel flag):

  1. Detect all technologies upfront
  2. Spawn one subagent per technology area with Task tool
  3. Each agent loads its skill and reviews its domain
  4. Wait for all agents
  5. Consolidate findings

Before Flagging Optimization or Pattern Issues

  1. Check CLAUDE.md for documented intentional patterns
  2. Check code comments around the flagged area for "intentional", "optimization", or "NOTE:"
  3. Trace the code path before claiming missing coverage or inconsistent handling
  4. Consider framework idioms - what looks wrong generically may be correct for the framework

Why: Analysis showed rejections where reviewers flagged "inconsistent error handling" that was intentional optimization, and "missing test coverage" for code paths that don't exist.

Step 7: Verify Findings

Before reporting any issue:

  1. Re-read the actual code (not just diff context)
  2. For "unused" claims - did you search all references?
  3. For "missing" claims - did you check framework/parent handling?
  4. For syntax issues - did you verify against current version docs?
  5. Remove any findings that are style preferences, not actual issues

Step 8: Review Convergence

Single-Pass Completeness

You MUST report ALL issues across ALL categories (style, logic, types, tests, security, performance) in a single review pass. Do not hold back issues for later rounds.

Before submitting findings, ask yourself:

  • "If all my recommended fixes are applied, will I find NEW issues in the fixed code?"
  • "Am I requesting new code (tests, types, modules) that will itself need review?"

If yes to either: include those anticipated downstream issues NOW, in this review, so the author can address everything at once.

Scope Rules

  • Review ONLY the code in the diff and directly related existing code
  • Do NOT request new features, test infrastructure, or architectural changes that didn't exist before the diff
  • If test coverage is missing, flag it as ONE Minor issue ("Missing test coverage for X, Y, Z") — do NOT specify implementation details like mock libraries, behaviour extraction, or dependency injection patterns that would introduce substantial new code
  • Typespecs, documentation, and naming issues are Minor unless they affect public API contracts
  • Do NOT request adding new dependencies (e.g. Mox, testing libraries, linter plugins)

Fix Complexity Budget

Fixes to existing code should be flagged at their real severity regardless of size.

However, requests for net-new code that didn't exist before the diff must be classified as Informational:

  • Adding a new dependency (e.g. Mox, a linter plugin)
  • Creating entirely new modules, files, or test suites
  • Extracting new behaviours, protocols, or abstractions

These are improvement suggestions for the author to consider in future work, not review blockers.

Iteration Policy

If this is a re-review after fixes were applied:

  • ONLY verify that previously flagged issues were addressed correctly
  • Do NOT introduce new findings unrelated to the previous review's issues
  • Accept Minor/Nice-to-Have issues that weren't fixed — do not re-flag them
  • The goal of re-review is VERIFICATION, not discovery

Output Format

## Review Summary

[1-2 sentence overview of findings]

## Issues

### Critical (Blocking)

1. [FILE:LINE] ISSUE_TITLE
   - Issue: Description of what's wrong
   - Why: Why this matters (bug, type safety, security)
   - Fix: Specific recommended fix

### Major (Should Fix)

2. [FILE:LINE] ISSUE_TITLE
   - Issue: ...
   - Why: ...
   - Fix: ...

### Minor (Nice to Have)

N. [FILE:LINE] ISSUE_TITLE
   - Issue: ...
   - Why: ...
   - Fix: ...

### Informational (For Awareness)

N. [FILE:LINE] SUGGESTION_TITLE
   - Suggestion: ...
   - Rationale: ...

## Good Patterns

- [FILE:LINE] Pattern description (preserve this)

## Verdict

Ready: Yes | No | With fixes 1-N (Critical/Major only; Minor items are acceptable)
Rationale: [1-2 sentences]

Post-Fix Verification

After fixes are applied, run:

ruff check .
mypy .
pytest

All checks must pass before approval.

Rules

  • Load skills BEFORE reviewing (not after)
  • Number every issue sequentially (1, 2, 3...)
  • Include FILE:LINE for each issue
  • Separate Issue/Why/Fix clearly
  • Categorize by actual severity
  • Run verification after fixes
  • Report ALL issues in a single pass — do not hold back findings for later iterations
  • Re-reviews verify previous fixes ONLY — no new discovery
  • Requests for net-new code (new modules, dependencies, test suites) are Informational, not blocking
  • The Verdict ignores Minor and Informational items — only Critical and Major block approval

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.57%
按下载量换算54

Claude

29.37%
按下载量换算45

Cursor

19.75%
按下载量换算30

Gemini CLI

10.57%
按下载量换算16

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/existential-birds/beagle --skill review-python 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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