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Agent Skill · 研究检索

review-python(审查 Python)

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。

review-python 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 56,324。

总安装 456周安装 19GitHub Stars 54下载 152核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/review-python

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/existential-birds/beagle --skill review-python;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

review-python 怎么安装?

在终端执行 npx skills add https://github.com/existential-birds/beagle --skill review-python。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

review-python 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

review-python 需要什么权限?

权限级别是执行命令。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。

review-python 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

review-python 用的人多吗?

公开数据是累计安装 456 次,近一周安装 19 次,下载 152 次,GitHub 54 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

review-python 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
review-python

审查 Python · 本页

45654执行命令未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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。目前只有一个公开来源,建议同时核对源仓库。

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