FFUF (Fuzz Faster U Fool) Skill workflow skill. Use this skill when the user needs Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
The command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
File Explorer
5 files
Showing SKILL.md
SKILL.md
Source instructions · Read-only preview
name
ffuf-web-fuzzing
description
FFUF (Fuzz Faster U Fool) Skill workflow skill. Use this skill when the user needs Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/ffuf-web-fuzzing from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses metadata.json plus ORIGIN.md as the provenance anchor for review.
FFUF (Fuzz Faster U Fool) Skill
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, Common Use Cases, Filtering and Matching, Rate Limiting and Timing, Output Options, Advanced Techniques.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
You are fuzzing web targets with ffuf during authorized security testing or penetration testing.
The task involves content discovery, subdomain enumeration, parameter fuzzing, or authenticated request fuzzing.
You need guidance on wordlists, filtering, calibration, and interpreting ffuf results efficiently.
Use when the request clearly matches the imported source intent: Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis.
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Use when provenance needs to stay visible in the answer, PR, or review packet.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
More from this repository
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
bash # Using Go go install github.com/ffuf/ffuf/v2@latest # Using Homebrew (macOS) brew install ffuf # Binary download # Download from: https://github.com/ffuf/ffuf/releases/latest
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Imported Workflow Notes
Imported: Installation
# Using Go
go install github.com/ffuf/ffuf/v2@latest
# Using Homebrew (macOS)
brew install ffuf
# Binary download# Download from: https://github.com/ffuf/ffuf/releases/latest
Imported: Overview
FFUF is a fast web fuzzer written in Go, designed for discovering hidden content, directories, files, subdomains, and testing for vulnerabilities during penetration testing. It's significantly faster than traditional tools like dirb or dirbuster.
Imported: Core Concepts
The FUZZ Keyword
The FUZZ keyword is used as a placeholder that gets replaced with entries from your wordlist. You can place it anywhere:
URLs: https://target.com/FUZZ
Headers: -H "Host: FUZZ"
POST data: -d "username=admin&password=FUZZ"
Multiple locations with custom keywords: -w wordlist.txt:CUSTOM then use CUSTOM instead of FUZZ
Multi-wordlist Modes
clusterbomb: Tests all combinations (default) - cartesian product
pitchfork: Iterates through wordlists in parallel (1-to-1 matching)
sniper: Tests one position at a time (for multiple FUZZ positions)
Examples
Example 1: Ask for the upstream workflow directly
Use @ffuf-web-fuzzing to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @ffuf-web-fuzzing against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @ffuf-web-fuzzing for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @ffuf-web-fuzzing using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Don't struggle with command-line flags for complex auth. Capture the full request and use --request:
# 1. Capture authenticated request from Burp/DevTools# 2. Save to req.txt with FUZZ keyword in place# 3. Run with -ac
ffuf --request req.txt -w wordlist.txt -ac -o results.json
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/ffuf-web-fuzzing, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Imported Troubleshooting Notes
Imported: Troubleshooting
Too Many False Positives
Use -ac for auto-calibration
Check default response and filter by size with -fs
Use regex filtering with -fr
Too Slow
Increase threads: -t 100
Reduce wordlist size
Use -ignore-body if you don't need response content
Getting Blocked
Reduce rate: -rate 2
Add delays: -p 0.5-1.5
Reduce threads: -t 10
Randomize User-Agent
Use proxy rotation
Missing Results
Check if you're filtering too aggressively
Use -mc all to see all responses
Disable auto-calibration temporarily
Use verbose mode -v to see what's happening
Related Skills
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@20-andruia-niche-intelligence-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@2d-games - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
ffuf -w wordlist.txt -X POST -d "param=FUZZ" -u https://target.com/endpoint
With Extensions
Add -e .php,.html,.txt
Filter Status
Add -fc 404,403
Filter Size
Add -fs 1234
Rate Limit
Add -rate 2
Save Output
Add -o results.json
Verbose
Add -c -v
Recursion
Add -recursion -recursion-depth 2
Through Proxy
Add -x http://127.0.0.1:8080
Imported: Additional Resources
This skill includes supplementary materials in the resources/ directory:
Resource Files
WORDLISTS.md: Comprehensive guide to SecLists wordlists, recommended lists for different scenarios, file extensions, and quick reference patterns
REQUEST_TEMPLATES.md: Pre-built req.txt templates for common authentication scenarios (JWT, OAuth, session cookies, API keys, etc.) with usage examples
Helper Script
ffuf_helper.py: Python script to assist with:
Analyzing ffuf JSON results for anomalies and interesting findings
Creating req.txt template files from command-line arguments
Generating number-based wordlists for IDOR testing
-mc: Match status codes (default: 200-299,301,302,307,401,403,405,500)
-ml: Match line count
-mr: Match regex
-ms: Match response size
-mt: Match response time (e.g., >100 or <100 milliseconds)
-mw: Match word count
Filters (Exclude Results)
-fc: Filter status codes (e.g., -fc 404,403,401)
-fl: Filter line count
-fr: Filter regex (e.g., -fr "error")
-fs: Filter response size (e.g., -fs 42,4242)
-ft: Filter response time
-fw: Filter word count
Auto-Calibration (USE BY DEFAULT!)
CRITICAL: Always use -ac unless you have a specific reason not to. This is especially important when having Claude analyze results, as it dramatically reduces noise and false positives.
# Auto-calibration - ALWAYS USE THIS
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -ac
# Per-host auto-calibration (useful for multiple hosts)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -ach
# Custom auto-calibration string (for specific patterns)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -acc "404NotFound"
Why -ac is essential:
Automatically detects and filters repetitive false positive responses
Removes noise from dynamic websites with random content
Makes results analysis much easier for both humans and Claude
Prevents thousands of identical 404/403 responses from cluttering output
Adapts to the target's specific behavior
When Claude analyzes your ffuf results, -ac is MANDATORY - without it, Claude will waste time sifting through thousands of false positives instead of finding the interesting anomalies.
Imported: Rate Limiting and Timing
Rate Control
# Limit to 2 requests per second (stealth mode)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -rate 2
# Add delay between requests (0.1 to 2 seconds random)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -p 0.1-2.0
# Set number of concurrent threads (default: 40)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -t 10
Time Limits
# Maximum total execution time (60 seconds)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -maxtime 60
# Maximum time per job (useful with recursion)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -maxtime-job 60 -recursion
Imported: Output Options
Output Formats
# JSON output
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -o results.json
# HTML output
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -of html -o results.html
# CSV output
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -of csv -o results.csv
# All formats
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -of all -o results
# Silent mode (no progress, only results)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -s
# Pipe to file with tee
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -s | tee results.txt
Imported: Advanced Techniques
Using Raw HTTP Requests (Critical for Authenticated Fuzzing)
This is one of the most powerful features of ffuf, especially for authenticated requests with complex headers, cookies, or tokens.
Workflow:
Capture a full authenticated request (from Burp Suite, browser DevTools, etc.)
Save it to a file (e.g., req.txt)
Replace the value you want to fuzz with the FUZZ keyword
Use the --request flag
# From a file containing raw HTTP request
ffuf --request req.txt -w /path/to/wordlist.txt -ac
