Why is the -ac flag important in this skill?

The auto-calibration (-ac) flag is critical because it automatically filters out repetitive false positive responses, allowing Claude to analyze only meaningful results.

FFUF (Fuzz Faster U Fool) is a fast web fuzzer written in Go, used by security professionals to find hidden content and vulnerabilities.

How does this skill improve Claude's performance?

It provides domain-specific patterns and best practices that prevent the AI from generating noisy or inefficient fuzzing commands.

Can I use this skill for authenticated testing?

Yes, the skill provides specific guidance on using raw HTTP requests to fuzz authenticated endpoints while maintaining sessions and tokens.

FFUF Web Fuzzing

Name: FFUF Web Fuzzing
Author: prof-ramos

byprof-ramos

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Security & Testing

Accelerates web security testing by providing expert guidance for high-performance fuzzing, directory discovery, and vulnerability research.

About

This skill empowers Claude to act as a security expert for FFUF (Fuzz Faster U Fool), a high-speed web fuzzer used in penetration testing. It provides specialized knowledge for discovering hidden files, directories, and subdomains, while also guiding users through complex scenarios like authenticated fuzzing via raw HTTP requests. By emphasizing auto-calibration and strategic filtering, the skill helps eliminate noise, ensuring that security researchers and developers can focus on identifying real vulnerabilities and sensitive data exposures efficiently.

Key Features

High-speed directory and file discovery using customizable wordlists
Intelligent filtering and auto-calibration to reduce false positives
1 GitHub stars
Multi-mode fuzzing strategies including clusterbomb and pitchfork
Authenticated fuzzing support through raw HTTP request templates
Advanced subdomain and virtual host enumeration techniques

Use Cases

Automating the identification of subdomains and virtual hosts for large-scale attack surface mapping
Performing mass parameter discovery to identify hidden API endpoints or potential IDORs
Discovering unlinked administrative panels or backup files on a target web server

About

Key Features

High-speed directory and file discovery using customizable wordlists
Intelligent filtering and auto-calibration to reduce false positives
1 GitHub stars
Multi-mode fuzzing strategies including clusterbomb and pitchfork
Authenticated fuzzing support through raw HTTP request templates
Advanced subdomain and virtual host enumeration techniques

Use Cases

Automating the identification of subdomains and virtual hosts for large-scale attack surface mapping
Performing mass parameter discovery to identify hidden API endpoints or potential IDORs
Discovering unlinked administrative panels or backup files on a target web server