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Agent Skill · 研究检索

lp-smart-code-review(lp 智能代码审查)

lp-smart-code-review 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

lp-smart-code-review 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,权限为需要联网;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 67,870。

总安装 240周安装 10GitHub Stars 公开资料未说明下载 80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:lp-smart-code-review(lp 智能代码审查)
来源仓库:https://github.com/lordprotein/lp-smart-code-core-review
仓库路径:skills/lp-smart-code-review
安装命令:
npx skills add https://github.com/lordprotein/lp-smart-code-core-review --skill lp-smart-code-review
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/lordprotein/lp-smart-code-core-review --skill lp-smart-code-review

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。

lp-smart-code-review 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于智能代码审查、自动化测试用例生成和质量评估等研究场景。通过关键词、任务或来源仓库进行信息筛选,需结合原始 README 确认具体用法。

安装命令为 npx skills add https://github.com/lordprotein/lp-smart-code-core-review --skill lp-smart-code-review。使用前建议确认权限范围、维护状态及是否触发联网或文件读写操作。

来源仓库内路径:skills/lp-smart-code-review

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/lordprotein/lp-smart-code-core-review --skill…;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

    它需要需要联网,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

lp-smart-code-review 怎么安装?

在终端执行 npx skills add https://github.com/lordprotein/lp-smart-code-core-review --skill lp-smart-c…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

lp-smart-code-review 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。

lp-smart-code-review 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

lp-smart-code-review 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。

lp-smart-code-review 用的人多吗?

公开数据是累计安装 240 次,近一周安装 10 次,下载 80 次。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。

lp-smart-code-review 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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

  • 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
lp-smart-code-review

lp 智能代码审查 · 本页

240—需要联网未明确未通过

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

Code Review Expert

Overview

Perform a structured review of the current git changes with focus on SOLID, architecture, removal candidates, and security risks. Default to review-only output unless the user asks to implement changes.

Severity Levels

LevelNameDescriptionAction
P0CriticalSecurity vulnerability, data loss risk, correctness bugMust block merge
P1HighLogic error, significant SOLID violation, performance regressionShould fix before merge
P2MediumCode smell, maintainability concern, minor SOLID violationFix in this PR or create follow-up
P3LowStyle, naming, minor suggestionOptional improvement

Workflow

1) Preflight context

  • Use git status -sb, git diff --stat, and git diff to scope changes.
  • If needed, use rg or grep to find related modules, usages, and contracts.
  • Identify entry points, ownership boundaries, and critical paths (auth, payments, data writes, network).

Edge cases:

  • No changes: If git diff is empty, inform user and ask if they want to review staged changes or a specific commit range.
  • Large diff (>500 lines): Summarize by file first, then review in batches by module/feature area.
  • Mixed concerns: Group findings by logical feature, not just file order.

2) SOLID + architecture smells

  • Load references/solid-checklist.md for specific prompts.
  • Look for:

- SRP: Overloaded modules with unrelated responsibilities. - OCP: Frequent edits to add behavior instead of extension points. - LSP: Subclasses that break expectations or require type checks. - ISP: Wide interfaces with unused methods. - DIP: High-level logic tied to low-level implementations.

  • When you propose a refactor, explain *why* it improves cohesion/coupling and outline a minimal, safe split.
  • If refactor is non-trivial, propose an incremental plan instead of a large rewrite.

3) Removal candidates + iteration plan

  • Load references/removal-plan.md for template.
  • Identify code that is unused, redundant, or feature-flagged off.
  • Distinguish safe delete now vs defer with plan.
  • Provide a follow-up plan with concrete steps and checkpoints (tests/metrics).

4) Security and reliability scan

  • Load references/security-checklist.md for coverage.
  • Check for:

- XSS, injection (SQL/NoSQL/command), SSRF, path traversal - AuthZ/AuthN gaps, missing tenancy checks - Secret leakage or API keys in logs/env/files - Rate limits, unbounded loops, CPU/memory hotspots - Unsafe deserialization, weak crypto, insecure defaults - Race conditions: concurrent access, check-then-act, TOCTOU, missing locks

  • Call out both exploitability and impact.

5) Code quality scan

  • Load references/code-quality-checklist.md for coverage.
  • Check for:

- Error handling: swallowed exceptions, overly broad catch, missing error handling, async errors - Performance: N+1 queries, CPU-intensive ops in hot paths, missing cache, unbounded memory - Boundary conditions: null/undefined handling, empty collections, numeric boundaries, off-by-one

  • Flag issues that may cause silent failures or production incidents.

6) Output format

Structure your review as follows:

## Code Review Summary

**Files reviewed**: X files, Y lines changed
**Overall assessment**: [APPROVE / REQUEST_CHANGES / COMMENT]

---

## Findings

### P0 - Critical
(none or list)

### P1 - High
1. **[file:line]** Brief title
  - Description of issue
  - Suggested fix

### P2 - Medium
2. (continue numbering across sections)
  - ...

### P3 - Low
...

---

## Removal/Iteration Plan
(if applicable)

## Additional Suggestions
(optional improvements, not blocking)

Inline comments: Use this format for file-specific findings:

::code-comment{file="path/to/file.ts" line="42" severity="P1"}
Description of the issue and suggested fix.
::

Clean review: If no issues found, explicitly state:

  • What was checked
  • Any areas not covered (e.g., "Did not verify database migrations")
  • Residual risks or recommended follow-up tests

7) Next steps confirmation

After presenting findings, ask user how to proceed:

---

## Next Steps

I found X issues (P0: _, P1: _, P2: _, P3: _).

**How would you like to proceed?**

1. **Fix all** - I'll implement all suggested fixes
2. **Fix P0/P1 only** - Address critical and high priority issues
3. **Fix specific items** - Tell me which issues to fix
4. **No changes** - Review complete, no implementation needed

Please choose an option or provide specific instructions.

Important: Do NOT implement any changes until user explicitly confirms. This is a review-first workflow.

Resources

references/

FilePurpose
solid-checklist.mdSOLID smell prompts and refactor heuristics
security-checklist.mdWeb/app security and runtime risk checklist
code-quality-checklist.mdError handling, performance, boundary conditions
removal-plan.mdTemplate for deletion candidates and follow-up plan

平台分布

Codex

35.52%
按下载量换算28

Claude

26.27%
按下载量换算21

Cursor

19.9%
按下载量换算16

Gemini CLI

9.51%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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