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Recon

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briiirussell
recon

Perform structured reconnaissance and attack surface enumeration for authorized penetration tests, CTF challenges, and bug bounty programs. Use when the user mentions 'recon,' 'reconnaissance,' 'enumerate,' 'attack surface,' 'subdomain enumeration,' 'port scan,' 'fingerprint,' 'asset discovery,' or needs to map a target's external footprint.

Overview

Publisherbriiirussell
Repositorycybersecurity-skills
Skill namerecon
Stars
429
Forks
60
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 briiirussell on GitHub. Read the source before you install it.

Installation

Install the Recon 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/briiirussell/cybersecurity-skills/tree/main/skills/recon
  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/briiirussell/cybersecurity-skills.git /tmp/cybersecurity-skills
mkdir -p .claude/skills
cp -r /tmp/cybersecurity-skills/skills/recon .claude/skills/recon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recon 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 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 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.

Recon — Penetration Testing Reconnaissance

Perform structured reconnaissance against an authorized target, organizing findings into an actionable attack surface map.

Cross-references: osint-recon for the deeper open-source-intelligence pass (people, organizations, historical data) — this skill is the active/passive target-mapping side, osint-recon is the broader investigative side; they pair naturally. web-pentest for the next stage once recon has produced an attack surface map and an authorized target list. owasp-audit for source-code review when you have access to the target's code.

Authorization Check

Before running any commands, confirm:

  1. The user has written authorization for the target (pentest engagement, bug bounty program, CTF/lab environment)
  2. The target is within the defined scope

If authorization is unclear, ask before proceeding. Never assume authorization.

Methodology

Phase 1: Passive Recon

Gather information without touching the target directly.

DNS enumeration:

  • Run dig any $ARGUMENTS for A, AAAA, MX, TXT, NS, CNAME records
  • Attempt zone transfer: dig axfr @ns-server $ARGUMENTS
  • Enumerate subdomains via certificate transparency:
    curl -s "https://crt.sh/?q=%25.$ARGUMENTS&output=json" | jq -r '.[].name_value' | sort -u

WHOIS and registration: Run whois $ARGUMENTS for registrant, nameserver, and creation date info.

Search engine dorking: Use targeted queries — site:, inurl:, filetype:, intitle: — to find exposed pages, documents, and admin panels.

Technology fingerprinting: Identify frameworks, CMS, server software, and JavaScript libraries from public-facing pages.

Public code repositories: Search GitHub/GitLab for the target's org name, domain, API keys, or internal paths.

Historical data: Check the Wayback Machine for old endpoints, removed pages, and configuration files.

Phase 2: Active Recon (explicit authorization only)

Port scanning:

bash
nmap -sC -sV -oN scan-results.txt $ARGUMENTS

Start with top 1000 ports. Expand to full range (-p-) if needed. Use -Pn if the host appears down but is in scope.

Service enumeration: Based on open ports, probe for version info and default configurations.

Web content discovery:

  • Directory bruting with gobuster, feroxbuster, or dirsearch
  • Virtual host enumeration
  • API endpoint discovery (check /api/, /v1/, /graphql, /swagger.json)

SSL/TLS analysis: Run testssl.sh or sslyze to check for weak ciphers, expired certificates, and misconfigurations.

Phase 3: Analysis

Correlate all findings. Identify the most promising attack vectors and prioritize by:

  1. Severity of potential impact
  2. Likelihood of exploitation
  3. Exposure level (internet-facing vs. internal)

Output Format

Produce a structured recon report:

markdown
# Recon Report
## Target: [target]
## Scope: [confirmed scope]
## Date: [date]

### Passive Findings
| Finding | Details | Relevance |
|---------|---------|-----------|

### Subdomains Discovered
- [list]

### Technologies Detected
- [list with versions where identified]

### Active Findings
| Port | Service | Version | Notes |
|------|---------|---------|-------|

### Attack Surface Summary
[Prioritized list of interesting findings with risk assessment]

### Recommended Next Steps
[Ordered list of what to investigate further]

Boundaries

  • Stay within the defined scope — never scan adjacent or out-of-scope systems
  • Rate-limit aggressive scans to avoid disruption
  • Log all commands run for the engagement record
  • If you discover evidence of active compromise by a third party, alert the user immediately
  • Refuse requests targeting systems without explicit authorization
  • Refuse requests for mass scanning of unrelated targets

References

  • PTES (Penetration Testing Execution Standard)
  • OWASP Testing Guide
  • Bug Bounty Methodology (jhaddix/tbhm)

Frequently asked questions

What does the Recon AI skill do?

Perform structured reconnaissance and attack surface enumeration for authorized penetration tests, CTF challenges, and bug bounty programs. Use when the user mentions 'recon,' 'reconnaissance,' 'enumerate,' 'attack surface,' 'subdomain enumeration,' 'port scan,' 'fingerprint,' 'asset discovery,' or needs to map a target's external footprint.

Why use Recon on TypingMind?

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

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

Which AI models can use Recon?

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?

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

Is the Recon AI skill free?

Yes. It is published on GitHub by briiirussell 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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