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Recon Dir Scan

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crazyMarky
recon-dir-scan

Directory and file enumeration using ffuf, gobuster, dirsearch, and feroxbuster. Use this skill when user needs to discover hidden directories, enumerate files, find backup files, or map application structure through path fuzzing.

Overview

PublishercrazyMarky
Repositorypentest-skills
Skill namerecon-dir-scan
Stars
342
Forks
37
Bundled files
11
LicenseApache-2.0
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by crazyMarky on GitHub. Read the source before you install it.

Installation

Install the Recon Dir Scan AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/crazyMarky/pentest-skills/tree/main/recon-dir-scan
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/crazyMarky/pentest-skills.git /tmp/pentest-skills
mkdir -p .claude/skills
cp -r /tmp/pentest-skills/recon-dir-scan .claude/skills/recon-dir-scan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recon Dir Scan in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Recon Dir Scan on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Recon Dir Scan is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Directory and File Enumeration

Authorization Warning

IMPORTANT: Directory scanning without proper authorization may be detected as intrusion attempts. Always ensure you have:

  • Written permission from the target application owner
  • Defined scope of authorized testing
  • Legal compliance with local regulations

Prerequisites

Required tools that must be installed on your system:

  • ffuf - go install github.com/ffuf/ffuf@latest
  • gobuster - go install github.com/OJ/gobuster/v3/cmd/gobusterdir@main

Optional tools:

  • feroxbuster - cargo install feroxbuster
  • dirsearch - pip install dirsearch
  • dirb - Package manager installation

Quick Start

Most commonly used commands for directory enumeration:

Fast Directory Scan (ffuf)

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ

Recursive Scan with Status Codes

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ -mc 200,301,302 -recursion

Gobuster Quick Scan

bash
gobuster dir -u https://target.com -w wordlist.txt -t 50

Common Scenarios

Scenario 1: Basic Directory Fuzzing

When you need to discover directories and files:

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ

Parameters:

  • -w wordlist.txt - Wordlist path
  • -u - Target URL with FUZZ keyword
  • -t - Threads (default: 40)

Example:

bash
ffuf -w /usr/share/seclists/Discovery/Web-Content/common.txt -u https://target.com/FUZZ

Scenario 2: Filter by Status Code

When you only want specific HTTP status codes:

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ -mc 200,301,302,403

Status Code Filters:

CodeMeaning
200OK (valid page)
301,302Redirects
403Forbidden (directory exists, no access)
401Authentication required

Negation filter (exclude codes):

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ -mc 200,204,301,302,307,401,403 -mc 404

Scenario 3: Recursive Scanning

When you need to scan discovered directories recursively:

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ -recursion -recursion-depth 2

Parameters:

  • -recursion - Enable recursive scanning
  • -recursion-depth - Maximum depth (default: 0)

Example:

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ -recursion -recursion-depth 3

Scenario 4: File Extension Fuzzing

When searching for specific file types:

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ -X .txt

Multiple extensions:

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ -X .txt,.php,.bak,.old

Extension from wordlist:

bash
# Format: extension:wordlist
ffuf -w extensions.txt:EXT -w words.txt:FUZZ -u https://target.com/FUZZ.EXT

Scenario 5: Hidden File Discovery

When looking for backup, config, or hidden files:

bash
# Hidden dotfiles
ffuf -w hidden_files.txt -u https://target.com/FUZZ

# Backup files
ffuf -w wordlist.txt -u https://target.com/FUZZ -X .bak,.backup,.old,.tmp,.swp

# Config files
ffuf -w config_files.txt -u https://target.com/FUZZ

Common hidden files to check:

.git
.env
.env.local
.env.backup
config.php.bak
wp-config.php.bak
.gitignore
.htaccess

Scenario 6: Virtual Host Discovery

When testing for virtual host routing:

bash
ffuf -w vhosts.txt -u https://target.com -H "Host: FUZZ.target.com"

Parameters:

  • -H - Add custom header
  • FUZZ in header value - Replaced by wordlist entries

Example:

bash
ffuf -w subdomains.txt -u http://192.168.1.100 -H "Host: FUZZ.example.com"

Scenario 7: API Endpoint Discovery

When enumerating API endpoints:

bash
ffuf -w api_endpoints.txt -u https://target.com/api/FUZZ

Common API patterns:

/api/v1/FUZZ
/api/v2/FUZZ
/graphql
/api/graphql
/rest/FUZZ

With HTTP methods:

bash
ffuf -w endpoints.txt -u https://target.com/api/FUZZ -X GET,POST,PUT,DELETE

Scenario 8: Parameter Fuzzing

When discovering hidden parameters:

bash
ffuf -w params.txt -u https://target.com/page?FUZZ=test

Value fuzzing:

bash
ffuf -w values.txt -u https://target.com/page?param=FUZZ

Scenario 9: Gobuster Scanning

Alternative to ffuf using gobuster:

bash
# Basic scan
gobuster dir -u https://target.com -w wordlist.txt -t 50

# With status filtering
gobuster dir -u https://target.com -w wordlist.txt -t 50 -k --status-codes 200,301,302,403

# Recursive
gobuster dir -u https://target.com -w wordlist.txt -t 50 -r

# With extensions
gobuster dir -u https://target.com -w wordlist.txt -t 50 -x php,txt,html

Scenario 10: Feroxbuster Scanning

Modern Rust-based directory scanner:

bash
# Basic scan
feroxbuster -u https://target.com -w wordlist.txt

# With recursion and status codes
feroxbuster -u https://target.com -w wordlist.txt -C 404 --depth 3

# Scan multiple URLs
feroxbuster -u https://target.com -u https://target2.com -w wordlist.txt

Tool Selection Guide

ScenarioRecommended ToolCommand
Quick scanffufffuf -w wordlist.txt -u https://target.com/FUZZ
Recursive scanffufffuf -w wordlist.txt -u https://target.com/FUZZ -recursion
Large wordlistgobustergobuster dir -u https://target.com -w wordlist.txt -t 100
Multi-targetferoxbusterferoxbuster -u https://target.com -w wordlist.txt
Hidden filesffufffuf -w files.txt -u https://target.com/FUZZ
API discoveryffufffuf -w api.txt -u https://target.com/api/FUZZ

Tool Comparison:

ToolLanguageSpeedFeaturesBest For
ffufGoVery FastHighly flexible, filteringMost scenarios
gobusterGoFastSimple, reliableQuick scans
feroxbusterRustFastMulti-target, recursionLarge assessments
dirsearchPythonMediumBuilt-in wordlistsBeginners

Common Wordlists

WordlistSizeDescriptionLocation
common.txt~4,600Common dirs/filesSecLists
raft-medium-directories~30,000Medium coverageSecLists
raft-large-directories~60,000Large coverageSecLists
directory-list-2.3-medium~220,000ComprehensiveDirBuster
apache.txt~5,000Apache defaultsSecLists
api-endpoints.txt~500API discoveryCustom

SecLists paths:

bash
/usr/share/seclists/Discovery/Web-Content/
/usr/share/seclists/Discovery/Web-Content/api/
/usr/share/seclists/Discovery/Web-Content/raft/

Advanced Techniques

Rate Limiting

Avoid detection by slowing requests:

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ -rate 100

User-Agent Rotation

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ \
  -H "User-Agent: Mozilla/5.0 (compatible; Googlebot/2.1)"

Authentication

bash
# Basic auth
ffuf -w wordlist.txt -u https://user:pass@target.com/FUZZ

# Header-based auth
ffuf -w wordlist.txt -u https://target.com/FUZZ \
  -H "Authorization: Bearer TOKEN"

Cookie/Session Based

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ \
  -H "Cookie: session=YOUR_SESSION_TOKEN"

Output Matching

Filter by response content:

bash
ffuf -w wordlist.txt -u https://target.com/FUZZ \
  -mr "admin"  # Match response containing "admin"

ffuf -w wordlist.txt -u https://target.com/FUZZ \
  -ms 1520    # Match specific response size

Output Formats

Save Results

bash
# ffuf JSON output
ffuf -w wordlist.txt -u https://target.com/FUZZ -o results.json

# ffuf plain output
ffuf -w wordlist.txt -u https://target.com/FUZZ -o results.txt

# Gobuster output
gobuster dir -u https://target.com -w wordlist.txt -o results.txt

Resume Scanning

bash
# ffuf resume
ffuf -w wordlist.txt -u https://target.com/FUZZ -resume-ffuf

Tips and Best Practices

  1. Start small - Use smaller wordlists first for quick wins
  2. Filter aggressively - Use -mc to reduce noise
  3. Check response sizes - Same size pages often indicate false positives
  4. Verify manually - Always check interesting results manually
  5. Rate limiting - Avoid blocking with appropriate delays
  6. Combine wordlists - Multiple wordlists for better coverage
  7. Check parameters - Don't forget to fuzz query parameters
  8. Look for 403s - Forbidden responses reveal valid paths

Resources

Scripts

  • scripts/ffuf_results_parser.py - Parse and filter ffuf JSON results
  • scripts/merge_wordlists.py - Merge and deduplicate multiple wordlists
  • scripts/status_code_analyzer.py - Analyze response patterns

References

  • references/ffuf_guide.md - Comprehensive ffuf reference
  • references/gobuster_guide.md - Gobuster usage documentation
  • references/wordlist_guide.md - Wordlist selection and creation

Scenario: Persistent Storage of Directory Scan Findings

When you need to persist directory scan findings to the database:

bash
# Manual entry after discovering directories
python .claude/skills/recon-dir-scan/scripts/dir_scan_storage.py \
  --host-ip 192.168.1.100 \
  --url "https://example.com" \
  --path "/admin" \
  --status 200 \
  --size 1234 \
  --subsystem "Web Application"

Parameters:

  • --host-ip - Target host IP (required)
  • --url - Base URL (required)
  • --path - Discovered path (required)
  • --status - HTTP status code (optional)
  • --size - Response size (optional)
  • --tool - Tool used (default: ffuf)
  • --subsystem - Subsystem name (optional)

Database location: ./data/results.db

Related skills: results-storage - Query data, generate reports

Assets

  • assets/common-dirs.txt - Common directory names
  • assets/common-files.txt - Common file names
  • assets/hidden-files.txt - Hidden and backup files
  • assets/api-endpoints.txt - Common API endpoints

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Recon Dir Scan AI skill do?

Directory and file enumeration using ffuf, gobuster, dirsearch, and feroxbuster. Use this skill when user needs to discover hidden directories, enumerate files, find backup files, or map application structure through path fuzzing.

Why use Recon Dir Scan on TypingMind?

Because you install it once and use it with any model. Recon Dir Scan is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Recon Dir Scan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/crazyMarky/pentest-skills/tree/main/recon-dir-scan. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Recon Dir Scan?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Recon Dir Scan?

As many as you like. As long as a model supports skills, you can use Recon Dir Scan with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Recon Dir Scan AI skill free?

Yes. It is published on GitHub by crazyMarky under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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