Is this skill suitable for CI/CD integration?

Absolutely; it includes capabilities for setting up and managing CodeQL within automated deployment and testing pipelines to catch vulnerabilities early.

Can I write custom queries with this skill?

Yes, this skill supports creating and running custom QL queries to find specific patterns or vulnerabilities unique to your project.

Who developed the CodeQL skill for Claude?

This specific skill implementation is maintained by Trail of Bits, a leading cybersecurity research firm known for advanced security tooling.

What is CodeQL used for in Claude Code?

CodeQL is used for deep static analysis to find security vulnerabilities and architectural flaws by querying code as if it were data.

Which programming languages does CodeQL support?

CodeQL supports a wide range of popular languages including C/C++, Java, Python, JavaScript, TypeScript, Go, and Ruby.

CodeQL Static Analysis

Name: CodeQL Static Analysis
Author: plurigrid

byplurigrid

•

Security & Testing

Performs deep semantic code analysis to detect security vulnerabilities, track data flow, and conduct comprehensive security audits.

About

CodeQL for Claude Code enables advanced static analysis by treating code as data to identify complex security flaws that traditional linters might miss. Developed by Trail of Bits, this skill allows users to generate CodeQL databases, execute custom QL queries, and perform sophisticated taint tracking across various programming languages. It is an essential tool for security researchers and developers who need to automate vulnerability discovery, conduct deep-dive security audits, or integrate robust static analysis into their CI/CD pipelines to prevent security regressions.

Key Features

Automated security vulnerability detection
Custom QL query execution and development
Advanced taint tracking and data flow analysis
CI/CD pipeline integration for automated security gates
CodeQL database generation from source code
2 GitHub stars

Use Cases

Automating security audits for large-scale enterprise codebases
Enforcing secure coding standards via custom static analysis rules and patterns
Identifying complex injection vulnerabilities through cross-functional data flow analysis

About

Key Features

Automated security vulnerability detection
Custom QL query execution and development
Advanced taint tracking and data flow analysis
CI/CD pipeline integration for automated security gates
CodeQL database generation from source code
2 GitHub stars

Use Cases

Automating security audits for large-scale enterprise codebases
Enforcing secure coding standards via custom static analysis rules and patterns
Identifying complex injection vulnerabilities through cross-functional data flow analysis