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commit-security-scan提交安全扫描

Agent Skill

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。

总安装

1,056

周安装

44

GitHub Stars

64

下载量

352
CodexClaudeCursorGemini CLI

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

unknown

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: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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

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。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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