Back to Skills

scanning-for-vulnerabilities

jeremylongshore
Updated Today
26 views
409
51
409
View on GitHub
Metaai

About

This skill enables automated vulnerability scanning for codebases using the vulnerability-scanner plugin. It identifies security issues through static code analysis, dependency checking for CVEs, and configuration analysis. Use it when developers need to scan for vulnerabilities, security flaws, or CVEs in their projects.

Documentation

Overview

This skill empowers Claude to automatically scan your codebase for security vulnerabilities. It leverages the vulnerability-scanner plugin to identify potential risks, including code-level flaws, vulnerable dependencies, and insecure configurations.

How It Works

  1. Initiate Scan: The skill activates the vulnerability-scanner plugin based on user input.
  2. Perform Analysis: The plugin scans the codebase, dependencies, and configurations for vulnerabilities, including CVE detection.
  3. Generate Report: The plugin creates a detailed vulnerability report with findings, severity levels, and remediation guidance.

When to Use This Skill

This skill activates when you need to:

  • Identify security vulnerabilities in your code.
  • Check your project's dependencies for known CVEs.
  • Review your project's configurations for security weaknesses.

Examples

Example 1: Identifying SQL Injection Risks

User request: "Scan my code for SQL injection vulnerabilities."

The skill will:

  1. Activate the vulnerability-scanner plugin.
  2. Analyze the codebase for potential SQL injection flaws.
  3. Generate a report highlighting any identified SQL injection risks and providing remediation steps.

Example 2: Checking for Vulnerable npm Packages

User request: "Check my project's npm dependencies for known vulnerabilities."

The skill will:

  1. Activate the vulnerability-scanner plugin.
  2. Scan the project's package.json file and identify any npm packages with known CVEs.
  3. Generate a report listing the vulnerable packages, their CVE identifiers, and recommended updates.

Best Practices

  • Regular Scanning: Run vulnerability scans regularly, especially before deployments.
  • Prioritize Remediation: Focus on addressing critical and high-severity vulnerabilities first.
  • Validate Fixes: After applying fixes, run another scan to ensure the vulnerabilities are resolved.

Integration

This skill integrates with the core Claude Code environment by providing automated vulnerability scanning capabilities. It can be used in conjunction with other plugins to create a comprehensive security workflow, such as integrating with a ticketing system to automatically create tickets for identified vulnerabilities.

Quick Install

/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus/tree/main/vulnerability-scanner

Copy and paste this command in Claude Code to install this skill

GitHub 仓库

jeremylongshore/claude-code-plugins-plus
Path: backups/skills-migration-20251108-070147/plugins/security/vulnerability-scanner/skills/vulnerability-scanner
aiautomationclaude-codedevopsmarketplacemcp

Related Skills

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill

llamaguard

Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

View skill

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill

langchain

Meta

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

View skill