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Agent Skill · 研究检索

commit-security-scan(提交安全扫描)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

commit-security-scan 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,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 42,619。

总安装 1,056周安装 44GitHub Stars 64下载 352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:commit-security-scan(提交安全扫描)
来源仓库:https://github.com/factory-ai/factory-plugins
仓库路径:skills/commit-security-scan
安装命令:
npx skills add https://github.com/factory-ai/factory-plugins --skill commit-security-scan
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/factory-ai/factory-plugins --skill commit-security-scan

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。

commit-security-scan 利用 LLM 推理分析代码更改,检测安全漏洞和 STRIDE 类别问题。适用于 PR 评审、提交前扫描和分支比较等安全辅助场景。需读取 .factory/threat-model.md 和应用仓库威胁模型模式,提供结构化分析。

不能将工具输出视为最终结论,建议确认威胁模型和敏感数据处理方式。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/commit-security-scan

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/factory-ai/factory-plugins --skill commit-secu…;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

commit-security-scan 怎么安装?

在终端执行 npx skills add https://github.com/factory-ai/factory-plugins --skill commit-security-scan。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

commit-security-scan 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。

commit-security-scan 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/factory-ai/factory-plugins --skill commit-security-scan 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。

commit-security-scan 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。

commit-security-scan 用的人多吗?

公开数据是累计安装 1,056 次,近一周安装 44 次,下载 352 次,GitHub 64 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。

commit-security-scan 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

  • 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

  • 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
commit-security-scan

提交安全扫描 · 本页

1,05664执行命令未明确未通过

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

Commit Security Scan

Analyze code changes (commits, PRs, diffs) using LLM-powered reasoning to detect security vulnerabilities. This skill reads code directly and applies patterns from the repository's threat model to identify issues across all STRIDE categories.

When to Use This Skill

  • PR review - Automated security scan on pull requests
  • Pre-commit check - Scan staged changes before committing
  • Branch comparison - Review security of feature branch changes
  • Code review assistance - Help reviewers spot security issues

Prerequisites

This skill requires:

  1. Threat model - .factory/threat-model.md must exist
  2. Security config - .factory/security-config.json for severity thresholds

IMPORTANT: If these files don't exist, you MUST generate them first before proceeding with the security scan.

To generate the prerequisites:

  1. Tell the user: "The threat model doesn't exist yet. I'll generate it first before scanning."
  2. Run the threat-model-generation skill to create both files
  3. Once complete, continue with the security scan

Do NOT ask the user to run the skill manually - just do it automatically as part of this workflow.

Inputs

The skill determines what to scan from the user's request:

Scan TypeHow to SpecifyExample
PR"Scan PR #123"Scan PR #456 for security vulnerabilities
Commit range"Scan commits X..Y"Scan commits abc123..def456
Single commit"Scan commit X"Scan commit abc123
Staged changes"Scan staged changes"Scan my staged changes for security issues
Uncommitted"Scan uncommitted changes"Scan working directory changes
Branch comparison"Scan from X to Y"Scan changes from main to feature-branch
Last N commits"Scan last N commits"Scan the last 3 commits

If no scope is specified, prompt the user for clarification.

Instructions

Follow these steps in order:

Step 1: Verify Prerequisites (Auto-Generate if Missing)

Try to read these files:

  • .factory/threat-model.md
  • .factory/security-config.json

If either file is missing or cannot be read:

  1. Inform the user: "The security threat model doesn't exist yet. I'll generate it first - this may take a minute."
  2. Invoke the threat-model-generation skill to analyze the repository and create both files
  3. Once generation completes, continue with Step 2

This ensures the security scan always has the threat model context it needs for accurate analysis.

Step 2: Get Changed Files

Based on the user's request, get the list of changed files and their diffs using git:

  • For PRs: use gh pr diff
  • For commits/ranges: use git diff or git show
  • For staged changes: use git diff --cached

Read the full content of each changed file for context.

Step 3: Load Threat Model

Read .factory/threat-model.md and .factory/security-config.json to understand:

  • The system's architecture and trust boundaries
  • Known vulnerability patterns for this codebase
  • Severity thresholds for findings

Step 4: Analyze for Vulnerabilities

For each changed file, systematically check for STRIDE threats:

S - Spoofing Identity
  • Missing or weak authentication checks
  • Session handling vulnerabilities
  • Token/credential exposure in code
  • Insecure cookie settings
T - Tampering with Data
  • SQL Injection: String concatenation/interpolation in SQL queries
  • Command Injection: User input in shell commands, eval(), exec()
  • XSS: Unescaped user input in HTML/templates
  • Mass Assignment: Blind assignment from request to model
  • Path Traversal: User input in file paths without validation
R - Repudiation
  • Missing audit logging for sensitive operations
  • Insufficient error logging
  • Log injection vulnerabilities
I - Information Disclosure
  • IDOR: Direct object access without ownership verification
  • Verbose error messages exposing internals
  • Hardcoded secrets, API keys, credentials
  • Sensitive data in logs or responses
  • Debug endpoints exposed
D - Denial of Service
  • Missing rate limiting on endpoints
  • Unbounded resource consumption (file uploads, queries)
  • Algorithmic complexity attacks (regex, sorting)
  • Missing pagination on list endpoints
E - Elevation of Privilege
  • Missing authorization checks on endpoints
  • Role/permission bypass opportunities
  • Privilege escalation through parameter manipulation

Step 5: Assess Each Finding

For each potential vulnerability:

  1. Trace data flow: Follow user input from source to sink

- Where does the input come from? (request params, body, headers, files) - Does it pass through validation/sanitization? - Where does it end up? (database, shell, response, file system)

  1. Check for existing mitigations:

- Is there validation elsewhere in the codebase? - Are there middleware/decorators that protect this code? - Does the framework provide automatic protection?

  1. Determine severity:

- CRITICAL: Remote code execution, auth bypass, data breach - HIGH: SQL injection, XSS, IDOR, privilege escalation - MEDIUM: Information disclosure, missing security headers - LOW: Best practice violations, minor issues

  1. Assess confidence:

- HIGH: Clear vulnerable pattern, direct data flow, no mitigations - MEDIUM: Possible vulnerability, some uncertainty about context - LOW: Suspicious pattern, likely has mitigations we can't see

Step 6: Generate Report

Create security-findings.json with this structure:

{
  "scan_id": "scan-YYYY-MM-DD-XXX",
  "scan_date": "<ISO 8601 timestamp>",
  "scan_type": "pr|commit|range|staged|working",
  "commit_range": "<base>..<head>",
  "pr_number": null,
  "threat_model_version": "<from security-config.json>",
  "findings": [
    {
      "id": "VULN-001",
      "severity": "HIGH",
      "stride_category": "Tampering",
      "vulnerability_type": "SQL Injection",
      "cwe": "CWE-89",
      "file": "src/api/users.py",
      "line_range": "45-49",
      "code_context": "<vulnerable code snippet>",
      "analysis": "<explanation of why this is vulnerable>",
      "exploit_scenario": "<how an attacker could exploit this>",
      "threat_model_reference": "Section 5.2 - SQL Injection",
      "existing_mitigations": [],
      "recommended_fix": "<how to fix the vulnerability>",
      "confidence": "HIGH",
      "reasoning": "<why this confidence level>"
    }
  ],
  "summary": {
    "total_findings": 0,
    "by_severity": { "CRITICAL": 0, "HIGH": 0, "MEDIUM": 0, "LOW": 0 },
    "by_stride": {
      "Spoofing": 0,
      "Tampering": 0,
      "Repudiation": 0,
      "InfoDisclosure": 0,
      "DoS": 0,
      "ElevationOfPrivilege": 0
    },
    "files_analyzed": 0
  }
}

Step 7: Report Results

  1. Save findings to security-findings.json
  2. Report summary to user (findings count by severity, triggered thresholds)
  3. Check severity thresholds from security-config.json and note if any are triggered

CWE Reference

Common CWE mappings for findings:

Vulnerability TypeCWE
SQL InjectionCWE-89
Command InjectionCWE-78
XSS (Reflected)CWE-79
XSS (Stored)CWE-79
Path TraversalCWE-22
IDORCWE-639
Missing AuthenticationCWE-306
Missing AuthorizationCWE-862
Hardcoded CredentialsCWE-798
Sensitive Data ExposureCWE-200
Mass AssignmentCWE-915
Open RedirectCWE-601
SSRFCWE-918
XXECWE-611
Insecure DeserializationCWE-502

Example Invocations

Scan a PR:

Scan PR #123 for security vulnerabilities

Scan staged changes before committing:

Scan my staged changes for security issues

Scan a feature branch:

Scan changes from main to feature/user-auth for vulnerabilities

Scan recent commits:

Scan the last 5 commits for security issues

References

  • Analysis examples: analysis-examples.md (in this skill directory)
  • Threat model: .factory/threat-model.md
  • Security config: .factory/security-config.json
  • OWASP Top 10
  • CWE Top 25

平台分布

Codex

34.88%
按下载量换算123

Claude

33.01%
按下载量换算116

Cursor

19.1%
按下载量换算67

Gemini CLI

9.36%
按下载量换算33

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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/factory-ai/factory-plugins --skill commit-security-scan 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。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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