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code-review-sr代码审查 SR

Agent Skill

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。

总安装

19,070

周安装

779

GitHub Stars

公开资料未说明

下载量

6,170
OpenClaw

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

MIT-0

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: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

简介

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。

SKILL.md

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.*


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能力 5

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。

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安装前确认

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