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sentinel

Consolidate security findings from code, dependencies, and infrastructure into unified JSON and Markdown reports.

3|Updated Apr 13, 2026
Search Tags:#sast#threat-modeling#codeql#semgrep#security-audit#sentinel#ai-analysis
One-click install
npx skills add https://github.com/0x1337c0d3/claude-security --skill sentinel-0x1337c0d3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sentinel
Source: https://github.com/0x1337c0d3/claude-security/tree/main/skills/sentinel
Command: npx skills add https://github.com/0x1337c0d3/claude-security --skill sentinel-0x1337c0d3

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Systematizes the detection and prioritization of security findings across code, dependencies, and infrastructure by combining AI-driven analysis with established SAST and taint tools to produce actionable remediation guidance.

Core Features & Use Cases

  • Orchestrates AI-driven OWASP analysis with Semgrep SAST and CodeQL taint analysis to generate a unified view of risk.
  • Cross-validates findings across multiple tools, producing prioritised reports and a machine-readable consolidated findings JSON for CI/CD pipelines.
  • Supports threat modeling and reporting for codebases, IaC, and dependencies, enabling teams to track remediation progress over time.

Quick Start

Trigger Sentinel to run a full security audit and generate a consolidated findings report.

Frequently Asked Questions about sentinel

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

FAQPage Schema
How do I consolidate SAST findings from Semgrep and CodeQL into a single security report?▼

Consolidate SAST findings by orchestrating AI-driven analysis that cross-validates Semgrep and CodeQL outputs to produce a unified findings JSON and Markdown risk report.

How does AI threat modeling integrate with OWASP Top 10 for code repository audits?▼

AI threat modeling applies OWASP Top 10 loaded references to codebase scans, identifying vulnerabilities and generating prioritized remediation guidance for code repositories.

Can I use this security audit approach for IaC and dependency scanning in CI/CD pipelines?▼

Yes, this security audit approach supports IaC and dependency scanning within CI/CD pipelines by identifying risks across code and infrastructure to output machine-readable consolidated findings.

Do I need jq and Semgrep installed to run taint analysis and generate risk insights?▼

Yes, you need jq and Semgrep installed as dependencies to execute pattern scans, perform taint analysis, and generate actionable risk insights effectively.

What is the best way to prioritize security findings across code and infrastructure?▼

The best way to prioritize security findings is to cross-validate results across multiple SAST tools, applying AI analysis to generate consolidated, prioritized risk reports.

Why does my security audit require loaded references like OWASP Top 10 and CodeQL taint analysis?▼

Your security audit requires loaded references like OWASP Top 10 and CodeQL taint analysis to accurately identify vulnerabilities, apply semantic queries, and cross-validate findings.

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