Hi-FullHouse

Hi-FullHouse / CyberSecurity-Skills

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一个由AI运维的网络安全Skill知识库

14
1
69% credibility
Found May 19, 2026 at 61 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

CyberSecurity-Skills is a comprehensive educational library of 195 cybersecurity skills organized into 39 modules. It follows industry standards like PTES, OWASP, and NIST to cover the full spectrum of security work—from information gathering and vulnerability testing through to incident response, digital forensics, and compliance. The library serves as both a learning roadmap for newcomers and a practical checklist for security professionals conducting authorized assessments. It includes both offensive (red team) and defensive (blue team) techniques, with clear disclaimers that all testing must be properly authorized.

How It Works

1
🔍 You discover a security skills library

You find a massive collection of 195 cybersecurity skills organized into 39 clear topics, from basic scanning to advanced defense.

2
📚 You explore the organized modules

The library is neatly arranged from information gathering through to reporting, plus specialized areas like cloud security, mobile testing, and incident response.

3
✨ You pick your learning path

Whether you're a beginner starting with basic concepts or an expert diving into advanced topics, each skill comes with explanations, examples, and reference materials.

4
You choose your focus area
🛡️
For defenders

Blue team skills cover threat hunting, incident response, security audits, and SOC operations.

⚔️
For attackers (authorized only)

Red team skills cover testing methodologies, but the library emphasizes that all testing must be authorized.

5
📋 You use the checklists for real work

Each skill is a structured checklist you can follow during actual security assessments, ensuring nothing gets missed.

✅ You build comprehensive security knowledge

You gain a well-organized reference that covers both attack and defense techniques, following industry standards for professional security work.

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Star Growth

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AI-Generated Review

What is CyberSecurity-Skills?

CyberSecurity-Skills is a comprehensive cybersecurity knowledge base written in Python that organizes 195 skills across 39 domains. It maps the complete security landscape from initial reconnaissance through post-exploitation, covering both defensive operations like SOC management and threat intelligence, as well as offensive techniques spanning web application testing, mobile security, and emerging areas like LLM and blockchain security. The system provides a CLI for querying skills by keyword or module ID, and includes manifests designed for AI agent integration so tools like Claude or Cursor can reference security best practices during assessments.

Why is it gaining traction?

The project stands out for its breadth—39 security domains in a single repository—organized around established standards like PTES and OWASP, making it useful for practitioners who need to quickly orient in unfamiliar areas. The AI agent integration is a differentiator: security teams can hook this into their AI assistants to pull curated knowledge during pentests or code reviews. The Chinese-language content with references to Chinese compliance frameworks (等保2.0) fills a gap for Chinese-speaking security professionals, though this may limit relevance for those focused on Western regulatory environments.

Who should use this?

Security practitioners expanding into new domains—someone moving from web testing into cloud or IoT assessments, or an incident responder needing structured guidance on LLM security. Penetration testers looking for a systematic checklist to ensure coverage across engagements. Security tool developers who want AI agents to access structured security knowledge during tasks. It is less useful for beginners seeking foundational tutorials or for organizations requiring heavily vetted, production-ready content.

Verdict

This is a solid reference library for experienced security professionals who need breadth over depth, particularly those working with AI coding assistants. At 14 stars and a credibility score of 0.699999988079071%, the project is early-stage—quality varies across modules and content has not been battle-tested by large communities. Contributions and community validation would strengthen confidence in accuracy. Use it as a starting point and verify critical guidance against established sources.

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