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Cms Detection

CommunityPopular
uphiago
cms-detection

Identify CMS, frameworks, and server technology stacks on live hosts.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill namecms-detection
Stars
1.3K
Forks
213
Bundled files
Instructions only
LicenseMIT
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Cms Detection 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.
  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/uphiago/recon-skills.git /tmp/recon-skills
mkdir -p .claude/skills
cp -r /tmp/recon-skills/recon/cms-detection .claude/skills/cms-detection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cms Detection 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 Cms Detection 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 Cms Detection 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.

CMS Detection

Identify the content management system, web framework, server software, and technology stack of every live host. Know which stack you're attacking before you attack it — a WordPress site needs different tests than a Laravel API or a Spring Boot microservice. Multi-CMS detection covers WordPress, Drupal, Joomla, Magento, Shopify, Wix, Squarespace, Laravel, Django, Express, Spring Boot, and more.

When to Use

  • You have a list of alive hosts and need to categorize them by technology.
  • WordPress-specific tests produced false positives (the site uses Drupal).
  • Need to identify which CMS version is running to match against known CVEs.
  • A host returns generic 200 on all paths — technology detection tells you what it actually runs.
  • Want to find sites running outdated versions of popular CMS platforms.

Prerequisites

  • terminal with httpx, whatweb, wappalyzer, and curl.
  • A list of alive subdomains from skill_view(name='visual-recon') or skill_view(name='subdomain-enumeration').

Quick Detection

bash
# Fastest: httpx with built-in tech detection
cat alive_subs.txt | httpx -silent -tech-detect -o tech_detect.txt

# Deep: whatweb with aggressive checks
whatweb -i alive_subs.txt -a 3 -t 50 --log-brief=cms_results.txt

Procedure

Phase 1 — Bulk Technology Fingerprinting

bash
# httpx — fast tech detection using Wappalyzer signatures
cat alive_subs.txt | httpx -silent -tech-detect -o tech_httpx.txt

# Parse results: extract unique technologies with counts
cat tech_httpx.txt | awk -F' [' '{print $2}' | tr -d ']' | tr ',' '\n' \
  | sed 's/^ *//' | sort | uniq -c | sort -rn > tech_summary.txt

# whatweb — deeper, identifies specific CMS versions
whatweb -i alive_subs.txt -a 3 -t 50 \
  --log-brief=cms_results.txt \
  --log-json=cms_results.json

# Parse whatweb JSON for versioned findings
cat cms_results.json | jq -r '.[] | select(.version != null) | "\(.target): \(.plugin) \(.version)"' \
  | sort -u > versioned_cms.txt

Phase 2 — CMS-Specific Version Detection

WordPress
bash
# Generator meta tag
curl --max-time 30 --connect-timeout 10 -sk "https://target.com" | grep -Eo '<meta name="generator"[^>]*content="WordPress [^"]+' | head -1

# RSS feed
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/feed/" | grep -Eo '<generator>https://wordpress.org/\?v=[^<]+' | head -1

# readme.html (often left accessible)
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/readme.html" | grep -Eo "Version [0-9.]+" | head -1

# wp-json namespace
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/wp-json/" | jq -r '.namespaces[]' 2>/dev/null
Drupal
bash
# CHANGELOG.txt (Drupal's canonical version leak)
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/CHANGELOG.txt" | head -5

# Drupal specific paths
curl --max-time 30 --connect-timeout 10 -skI "https://target.com/user/login" | grep -i "drupal\|x-generator"
curl --max-time 30 --connect-timeout 10 -skI "https://target.com/node/1"
Joomla
bash
# Joomla version files
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/administrator/manifests/files/joomla.xml" | grep -Eo '<version>[^<]+' | head -1
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/language/en-GB/en-GB.xml" | grep -Eo '<version>[^<]+'

# Meta tag
curl --max-time 30 --connect-timeout 10 -sk "https://target.com" | grep -Eo '<meta name="generator"[^>]*content="Joomla[^"]+' | head -1
Magento
bash
# Magento version file
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/magento_version" | head -1
# Composer lock
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/composer.lock" | jq -r '.packages[] | select(.name=="magento/magento2-base") | .version' 2>/dev/null
Laravel
bash
# Laravel debug info
curl --max-time 30 --connect-timeout 10 -sk "https://target.com" | grep -Eo 'Laravel v[0-9.]+'
# Composer lock
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/composer.lock" | jq -r '.packages[] | select(.name=="laravel/framework") | .version' 2>/dev/null

Phase 3 — Server and Framework Detection

bash
# Server header — often removed but check anyway
for host in $(cat alive_subs.txt); do
  echo -n "$host: "
  curl --max-time 30 --connect-timeout 10 -skI "https://$host" | grep -i "server:\|x-powered-by:" | tr '\n' ' '
  echo
  sleep 0.3
done > server_headers.txt

# Common server signatures and what they mean
# nginx → likely PHP-FPM or Node.js proxy
# Apache → shared hosting, cPanel
# IIS → Windows, ASP.NET
# LiteSpeed → shared hosting, WordPress optimized
# cloudflare → CDN/WAF in front, need origin-ip-discovery
# Microsoft-IIS/10.0 → Windows Server 2016+

# Framework detection via specific paths
# Django admin: /admin/ → redirect to /admin/login/
# Rails: /assets/application-*.js pattern
# Express: X-Powered-By: Express
# Spring Boot: /actuator/health returns {"status":"UP"}
# Next.js: /_next/static/ chunks

Phase 4 — Technology Stack Categorization

bash
# Categorize hosts by CMS for targeted testing
echo "=== WORDPRESS ==="
grep -i "wordpress" tech_detect.txt

echo "=== DRUPAL ==="
grep -i "drupal" tech_detect.txt

echo "=== OTHER CMS ==="
grep -iE "joomla|magento|shopify|wix|squarespace|ghost" tech_detect.txt

echo "=== FRAMEWORKS ==="
grep -iE "laravel|django|rails|express|spring|next\.js|nuxt" tech_detect.txt

echo "=== E-COMMERCE ==="
grep -iE "woocommerce|magento|shopify|prestashop|opencart" tech_detect.txt

echo "=== NO CMS (Static/Custom) ==="
cat alive_subs.txt | httpx -silent -td \
  | grep -v -iE "wordpress|drupal|joomla|magento|laravel|django|rails"

Phase 5 — Version → CVE Mapping

bash
# Map detected versions to known CVEs
# WordPress: check https://wpscan.com/wordpress-security/vulnerability-database/
# Drupal: check https://www.drupal.org/security
# Magento: check https://magento.com/security/patches

# searchsploit — local ExploitDB search
searchsploit wordpress 6.5
searchsploit drupal 10.2
searchsploit joomla 5.1

# nuclei — automated CVE detection on detected CMS
nuclei -l wordpress_sites.txt -t nuclei-templates/http/cves/2024/

Pitfalls

  • Generator meta tags can be spoofed. A site claiming "WordPress 7.0" may be running 6.5. Cross-check with readme.html or RSS feed.
  • Technology detection is signature-based. Custom themes may strip or modify signatures.
  • CDN caching may serve stale version info. Force cache bypass with random query parameters.
  • whatweb can trigger WAF blocks on aggressive scans. Use -a 2 (less aggressive) on protected targets.
  • Headless CMS (Strapi, Contentful) has no version file. Look for API endpoints instead.

Verification

  1. At least two independent methods confirm the same CMS/version (meta tag + readme + wp-json).
  2. Version-specific paths exist (e.g., /wp-admin/ for WordPress, /user/login for Drupal).
  3. Framework-specific endpoints respond correctly (e.g., /actuator/health for Spring Boot).
  4. Document: hostname, CMS, version, detection method, and whether the version has known CVEs.

Related Skills

  • web-enumeration — Once CMS is identified, enumerate its specific paths and endpoints.
  • wordpress-plugin-hunt — Deep plugin version detection for WordPress targets.
  • wp-mass-recon — Batch WordPress vulnerability scanning.
  • visual-recon — Confirm CMS detection with visual screenshot verification.
  • deep-invade — Run nuclei CVE scans against detected versions.

Frequently asked questions

What does the Cms Detection AI skill do?

Identify CMS, frameworks, and server technology stacks on live hosts.

Why use Cms Detection on TypingMind?

Because you install it once and use it with any model. Cms Detection 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 Cms Detection in TypingMind?

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

Which AI models can use Cms Detection?

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 Cms Detection?

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

Is the Cms Detection AI skill free?

Yes. It is published on GitHub by uphiago under the MIT 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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