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Agent Skill · 研究检索

code-review-sr(代码审查 SR)

code-review-sr 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

code-review-sr 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,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 7,412。

总安装 19,070周安装 779GitHub Stars 公开资料未说明下载 6,170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
MIT-0
版本
1.0.4

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:code-review-sr(代码审查 SR)
来源仓库:https://github.com/theshadowrose/code-review-sr
安装命令:
openclaw skills install code-review-sr
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install code-review-sr

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。

结合本地静态分析与深度 AI 推理的智能代码审查系统。快速捕获错误、安全漏洞、性能和风格问题并提供修复方案。适用于高要求场景下的精细化代码质量把控。code-review-sr 属于研究检索类 Skill,可作为该场景下的辅助能力补充。依赖本地分析引擎,需确保环境满足计算资源需求。

输出结果应结合上下文综合判断,不可盲目采纳。

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 OpenClaw;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 openclaw skills install code-review-sr;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

    它需要接触敏感数据,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

code-review-sr 怎么安装?

在终端执行 openclaw skills install code-review-sr。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

code-review-sr 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

code-review-sr 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

code-review-sr 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

code-review-sr 用的人多吗?

公开数据是累计安装 19,070 次,近一周安装 779 次,下载 6,170 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。

code-review-sr 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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 OpenClaw,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
code-review-sr

代码审查 SR · 本页

19,070—接触敏感数据MIT-0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

slug
code-review-sr
name
CodeReview Automated Code Review Assistant
description
AI-powered code review that combines fast local static analysis with deep AI reasoning. Catches bugs, security vulnerabilities, performance issues, and style problems. Supports Anthropic, OpenAI, and Ollama models. Falls back to local regex analysis when offline.
author
@TheShadowRose
version
1.0.4
tags
["code-review", "ai", "static-analysis", "security"]
license
MIT
env
ANTHROPIC_API_KEY
Optional - for Anthropic/Claude models (or set CLAUDE_API_KEY)
OPENAI_API_KEY
Optional - for OpenAI/GPT models
OLLAMA_HOST
Optional - Ollama base URL, default http://localhost:11434
OLLAMA_PORT
Optional - Ollama port, default 11434

CodeReview — AI-Powered Code Review Assistant

Combines fast local regex pattern matching with deep AI-powered analysis to deliver thorough, actionable code reviews. Runs a local static analysis pre-pass first, then sends code and initial findings to an AI model for comprehensive review including bug detection, security analysis, performance suggestions, and style feedback.


How It Works

  1. Local Pre-Pass — Regex-based pattern matching runs instantly, catching hardcoded secrets, eval usage, SQL injection patterns, empty catch blocks, long functions, and more.
  2. AI Deep Review — The full source code and local findings are sent to your chosen AI model (Anthropic, OpenAI, or Ollama) for deep reasoning about bugs, logic errors, performance, and architecture.
  3. Graceful Fallback — If no API key is set or the AI call fails, you still get local static analysis results. Never blocks your workflow.

Usage

const { CodeReview } = require('./src/code-review');

// AI-powered review (default: anthropic/claude-haiku-4-5)
const reviewer = new CodeReview({ model: 'anthropic/claude-haiku-4-5' });
const result = await reviewer.review('./src/auth.js');

console.log(result.score);        // 1-10
console.log(result.issues);       // Array of issues with severity, line, type, message
console.log(result.suggestions);  // Actionable improvement suggestions
console.log(result.summary);      // Concise quality summary
console.log(result.aiPowered);    // true

// Review an entire directory
const dirResult = await reviewer.reviewDir('./src', {
  include: ['*.js', '*.ts'],
  exclude: ['node_modules', '.git', 'dist'],
  concurrency: 3
});
console.log(dirResult.averageScore);
console.log(dirResult.totalIssues);

Model Options

ProviderExampleAPI Key Env Var
Anthropicanthropic/claude-haiku-4-5ANTHROPIC_API_KEY
OpenAIopenai/gpt-4o-miniOPENAI_API_KEY
Ollama (local)ollama/llama3None required
// OpenAI
const reviewer = new CodeReview({ model: 'openai/gpt-4o-mini' });

// Local Ollama
const reviewer = new CodeReview({ model: 'ollama/codellama' });

// Local-only (no AI, regex patterns only)
const reviewer = new CodeReview();
const result = await reviewer.review('./src/app.js');
// result.aiPowered === false

What It Catches

CategoryExamples
BugsNull references, off-by-one errors, race conditions, empty catch blocks
SecuritySQL injection, XSS, hardcoded secrets, eval usage
PerformanceN+1 queries, unnecessary loops, memory leaks
StyleInconsistent naming, long functions, dead code, console.log in production
LogicUnreachable code, redundant conditions
MaintainabilityDeeply nested callbacks, magic numbers, TODO/FIXME markers

Output Format

{
  "file": "./src/auth.js",
  "score": 5,
  "issues": [
    {
      "severity": "high",
      "line": 42,
      "type": "security",
      "message": "User input passed directly to SQL query without parameterization"
    },
    {
      "severity": "medium",
      "line": 87,
      "type": "bugs",
      "message": "Empty catch block silently swallows database connection errors"
    }
  ],
  "suggestions": [
    "Use parameterized queries or an ORM to prevent SQL injection on line 42",
    "Add error logging in the catch block on line 87",
    "Extract the authentication logic into a separate middleware module"
  ],
  "summary": "The auth module has a critical SQL injection vulnerability and several error handling gaps. Core logic is sound but needs security hardening.",
  "totalIssues": 2,
  "lines": 142,
  "aiPowered": true,
  "model": "anthropic/claude-haiku-4-5"
}

Language Support

Works with any language your AI model understands. The local pre-pass targets common patterns across languages. AI review tested with:

JavaScript, TypeScript, Python, Go, Rust, Java, C#, Ruby, PHP, Swift, Kotlin

Technical Details

  • Zero npm dependencies — Pure Node.js using only built-in https, http, fs, and path modules
  • File truncation — Files are truncated at 8,000 characters before sending to AI to stay within token limits
  • Concurrency control — Directory reviews process files in configurable parallel batches (default: 3)
  • Graceful degradation — AI failures never crash; local results are always available

⚠️ Disclaimer

This software is provided "AS IS", without warranty of any kind, express or implied.

USE AT YOUR OWN RISK.

  • The author(s) are NOT liable for any damages, losses, or consequences arising from

the use or misuse of this software — including but not limited to financial loss, data loss, security breaches, business interruption, or any indirect/consequential damages.

  • This software does NOT constitute financial, legal, trading, or professional advice.
  • Users are solely responsible for evaluating whether this software is suitable for

their use case, environment, and risk tolerance.

  • No guarantee is made regarding accuracy, reliability, completeness, or fitness

for any particular purpose.

  • The author(s) are not responsible for how third parties use, modify, or distribute

this software after purchase.

By downloading, installing, or using this software, you acknowledge that you have read this disclaimer and agree to use the software entirely at your own risk.

DATA DISCLAIMER: When an AI model is configured, this software sends your source code and static analysis findings to the configured provider (Anthropic, OpenAI, or a local Ollama instance). Do not run it over code containing secrets or sensitive data unless you understand where data is sent. Without an API key, all analysis is local-only. The author(s) are not responsible for data loss, corruption, or unauthorized access resulting from software bugs, system failures, or user error. Always maintain independent backups of important data. When AI models are configured, file contents are sent to the respective AI provider's API (Anthropic, OpenAI, or your local Ollama instance). No data is transmitted externally when running in local-only mode (no model configured).


Support & Links

🐛 Bug ReportsTheShadowyRose@proton.me
☕ Ko-fiko-fi.com/theshadowrose
🛒 Gumroadshadowyrose.gumroad.com
🐦 Twitter@TheShadowyRose
🐙 GitHubgithub.com/TheShadowRose
🧠 PromptBasepromptbase.com/profile/shadowrose

*Built with OpenClaw — thank you for making this possible.*


🛠️ Need something custom? Custom OpenClaw agents & skills starting at $500. If you can describe it, I can build it. → Hire me on Fiverr

平台分布

OpenClaw

94.38%
按下载量换算5,823

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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