Claude-Red

Product Introduction

1. Definition: Claude-Red is a curated, open-source library of offensive security skills designed for the Claude AI model's skills system. It is a technical repository of structured SKILL.md files that function as expert-level prompt engineering modules for penetration testing and red teaming.
2. Core Value Proposition: It exists to transform the general-purpose Claude AI assistant into a context-aware, domain-specialized red team operator. Its primary value is providing on-demand, expert-level cybersecurity methodology without requiring constant manual prompting, thereby accelerating authorized security assessments, vulnerability research, and exploit development workflows.

Main Features

1. Structured SKILL.md Files: Each skill is a self-contained Markdown file following a strict template. It primes Claude with a specific attack methodology, including techniques, tool commands (e.g., for sqlmap, hashcat, Mimikatz), common bypasses, escalation paths, and real-world CVEs. This structure allows Claude to generate contextually accurate and actionable security guidance.
2. Context-Aware, On-Demand Loading: The Claude skills system loads skills based on conversational triggers. Mentioning "SQL injection" automatically loads the offensive-sqli skill, priming Claude with database-specific payloads and filter bypass techniques without bloating the initial context window. This is a core technical mechanism for maintaining efficiency.
3. Comprehensive Offensive Security Taxonomy: The library is organized into over 20 technical categories covering the entire cyber kill chain. It spans from initial reconnaissance (offensive-osint) and web app attacks (offensive-ssrf, offensive-ssti) to post-exploitation (offensive-lateral-movement), forensics evasion (offensive-anti-forensics), and specialized domains like IoT (offensive-iot) and wireless (offensive-wpa2-psk, offensive-bluetooth-ble).
4. Integration with Claude's Ecosystem: Skills can be deployed via the native Claude desktop app (auto-loaded from ~/.claude/skills/), used with Claude Code via the command line by piping skill content as a system prompt, or manually integrated into Claude.ai projects. The claude-skills.json file provides a manifest for the ecosystem.

Problems Solved

1. Pain Point: The time-consuming process of manually providing complex, context-specific red teaming methodology to a general AI model for each new task or attack surface during security engagements.
2. Target Audience: Penetration testers, red teamers, vulnerability researchers, CTF players, security engineers validating controls, and bug bounty hunters seeking to automate and enhance their technical workflow with AI assistance.
3. Use Cases: Rapidly generating tailored SQL injection payloads for an unusual DBMS during a web app test; formulating a step-by-step Kerberoasting and ACL abuse attack path in an Active Directory environment; developing a custom EDR evasion technique using indirect syscalls and PPID spoofing; creating a fuzzing harness for a novel network protocol.

Unique Advantages

1. Strengths & Limitations (Pros & Cons):

   • Pros: Dramatically reduces prompt engineering overhead for technical security tasks. Provides a vast, community-vetted knowledge base of offensive techniques. Open-source and extensible, allowing users to create or modify skills. On-demand loading preserves AI context window for longer, more complex interactions. Serves as an excellent training resource for learning attack methodologies.
   • Cons: Effectiveness is inherently tied to the underlying AI model's (Claude's) capabilities and knowledge cutoff; it cannot execute code itself. Requires a basic understanding of the Claude skills system to install and use effectively. The quality and depth of individual skills may vary. It is a tool for planning and guidance, not an autonomous exploitation framework.

2. Key Alternatives & Differentiation:

   • Manual Prompting: The alternative is manually crafting detailed, technical prompts for each task. Claude-Red differentiates by providing pre-engineered, optimized, and structured prompts, saving significant time and ensuring methodological completeness.
   • General Security Chatbots (e.g., GPTs with web search): These lack the deep, pre-loaded context and structured methodology files. Claude-Red is purpose-built, offering more reliable and detailed technical outputs for offensive security without hallucinating tool flags or techniques as often.
   • Automated Penetration Testing Frameworks (e.g., Metasploit, Cobalt Strike): These are execution engines. Claude-Red is a complementary planning and knowledge system. It helps operators understand how and why to use those tools effectively, craft custom exploits, and navigate complex manual attack chains that automated tools cannot handle.

Frequently Asked Questions (FAQ)

1. Is Claude-Red legal to use? Claude-Red is a knowledge library for AI prompt engineering. Its legality depends entirely on the context of use. It is intended for authorized security testing, penetration testing with explicit permission, educational purposes in controlled environments (like CTFs or labs), and legitimate security research. Unauthorized use against systems you do not own is illegal.
2. How do I install Claude-Red skills for Claude Desktop? Clone the repository directly into your Claude skills directory using the command: git clone https://github.com/SnailSploit/claude-red ~/.claude/skills/claude-red. Claude Desktop will automatically detect and index the skills for on-demand use.
3. What is the difference between a Claude-Red skill and a standard prompt? A Claude-Red SKILL.md file is a structured, comprehensive prompt module that includes defined capabilities, detailed methodologies, tool command examples, and edge cases. It's engineered to make Claude adopt a specific "expert persona" (e.g., a WPA3 cracker, an Active Directory specialist), whereas a standard prompt is typically a one-off instruction.
4. Can I use Claude-Red with ChatGPT or other LLMs? The SKILL.md files are written in plain text. While designed for Claude's system, the technical content can be copied and adapted as a system prompt or context for other LLMs like ChatGPT or Google Gemini, though integration will not be as seamless as with the native Claude skills system.
5. How can I contribute a new skill to Claude-Red? Follow the guidelines in CONTRIBUTING.md. Contributions require creating a new SKILL.md file in the appropriate category directory using the provided template, which includes required frontmatter (name, category, description) and a structured format covering overview, capabilities, and methodology.