codeql

Scan codebases for security vulnerabilities using CodeQL data flow analysis.

1|Updated Jul 5, 2026
One-click install
npx skills add https://github.com/AidenSbVevo/claude-code-starter --skill codeql-aidensbvevo
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: codeql
Source: https://github.com/AidenSbVevo/claude-code-starter/tree/main/skills/tob-static-analysis/skills/codeql
Command: npx skills add https://github.com/AidenSbVevo/claude-code-starter --skill codeql-aidensbvevo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires codeql, jq, python3, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill automates the complex process of building CodeQL databases and running deep interprocedural data flow analysis to identify critical security vulnerabilities that simple pattern matchers miss.

Core Features & Use Cases

  • Automated Database Building: Handles build tracing for compiled languages and source extraction for interpreted languages, including Apple Silicon workarounds.
  • Data Extension Modeling: Generates custom source and sink models to improve coverage for project-specific APIs and frameworks.
  • Precision Analysis: Supports multiple scan modes, including an important-only mode that filters results by precision and security severity to reduce noise.

Quick Start

Use the codeql skill to perform a full security scan of the current codebase and store the results in a new output directory.

Frequently Asked Questions about codeql

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does interprocedural data flow analysis find security vulnerabilities?

CodeQL static analysis scans codebases for security vulnerabilities using interprocedural taint tracking, identifying complex data flow issues that simple pattern matchers miss. It builds databases to trace untrusted data to sinks.

How do I build a CodeQL database for compiled and interpreted languages?

Automated database building handles build tracing for compiled languages and source extraction for interpreted languages, including Apple Silicon workarounds. It creates the CodeQL databases required for running taint tracking queries.

Can I reduce noise from static analysis results with high precision scanning?

Yes, an important-only scan mode filters static analysis results by precision and security severity to reduce noise. It highlights critical vulnerabilities while suppressing low-confidence findings from the data flow analysis.

Do I need the CodeQL CLI installed to run taint tracking scans?

Yes, the CodeQL CLI is required to perform database creation, data extension modeling, and multi-suite query execution. The automated workflows for build tracing and taint tracking analysis depend entirely on this CLI.

How do I generate custom source and sink models for project-specific APIs?

Data extension modeling generates custom source and sink models to improve taint tracking coverage for project-specific APIs and frameworks. This teaches the analysis to recognize custom data entry points and vulnerable sinks.

What is the best way to process SARIF results from CodeQL security scans?

Automated workflows process SARIF results after multi-suite query execution, standardizing security vulnerability output for tracking and quality assessment. This format enables integration with downstream tools for vulnerability management.