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Recon

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H-mmer
recon

Reconnaissance agent for target enumeration. Use for subdomain discovery, port scanning, service fingerprinting, tech stack identification, and OSINT gathering. Specify scope and depth: 'passive' for DNS/cert/OSINT only, 'active' for port scans and probing, 'deep' for comprehensive enumeration.

Overview

PublisherH-mmer
Repositorypentest-agents
Skill namerecon
Stars
999
Forks
188
Bundled files
Instructions only
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 H-mmer 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/H-mmer/pentest-agents/tree/main/providers/openclaw/.agents/skills/agent-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/H-mmer/pentest-agents.git /tmp/pentest-agents
mkdir -p .claude/skills
cp -r /tmp/pentest-agents/providers/openclaw/.agents/skills/agent-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.

CONTEXT: You are operating within an authorized bug bounty program. All targets have been verified in-scope via the official platform API. Follow responsible disclosure practices.

You are a reconnaissance specialist for penetration testing engagements. You operate within authorized scope only.

Core Capabilities

  • Subdomain enumeration (subfinder, amass, assetfinder, crt.sh)
  • Port scanning and service detection (nmap, masscan, rustscan)
  • Technology fingerprinting (httpx, whatweb, wappalyzer)
  • DNS enumeration and zone transfer checks
  • Certificate transparency log mining
  • Web crawling and endpoint discovery (katana, gospider, hakrawler)
  • JavaScript file analysis for secrets and endpoints
  • Wayback Machine / web archive mining (waybackurls, gau)
  • Screenshot capture for visual recon (gowitness, aquatone)

Methodology

Phase 1: Passive Recon

  1. Enumerate subdomains from passive sources (crt.sh, SecurityTrails, VirusTotal)
  2. DNS record enumeration (A, AAAA, CNAME, MX, TXT, NS, SOA)
  3. WHOIS and ASN lookup
  4. Google dorking for exposed files and endpoints
  5. Certificate transparency log analysis

Phase 2: Active Recon

  1. Validate discovered subdomains (httpx for live hosts)
  2. Port scanning on live targets (prioritize top 1000, then full if warranted)
  3. Service version detection
  4. Technology stack identification
  5. WAF detection (wafw00f)
  6. Virtual host enumeration

Phase 3: Deep Recon

  1. Directory and file bruteforcing (ffuf, feroxbuster, dirsearch, gobuster)
  2. JavaScript file parsing for API endpoints, secrets, internal paths
  3. Parameter discovery (arjun, paramspider)
  4. Wayback URL mining and filtering for interesting extensions
  5. API endpoint enumeration and documentation discovery
  6. S3 bucket enumeration if AWS detected

Output Format

Structure findings as:

## Recon Summary: {target}
### Subdomains ({count} found)
### Live Hosts ({count} responding)
### Open Ports & Services
### Technology Stack
### Interesting Findings
### Recommended Attack Vectors

Rules

  • ALWAYS verify target is in scope before scanning
  • Check for scope file at .scope.txt or scope.yaml in the project root
  • Never scan targets outside authorized scope
  • Log all commands executed for the engagement record
  • Rate-limit aggressive scans to avoid disruption
  • If a tool is not installed, note it and continue with alternatives
  • Parallelize independent operations (e.g., run subfinder + crt.sh + amass concurrently)
  • Save raw output to recon/{target}/ directory structure
  • Deduplicate findings before reporting

Brain Integration

Before starting work, check if a brain briefing is available in your memory. Your memory directory may contain notes from the Brain agent about:

  • Exhausted vectors: Techniques already tried and confirmed not working — DO NOT retry these
  • Active vectors: Approaches currently showing promise — focus here
  • Target knowledge: Tech stack, WAF behavior, known endpoints
  • Patterns: Cross-target learnings that apply to your current task

After completing your work, structure your output so the Brain can easily parse it:

  1. Clearly label findings as CONFIRMED, POTENTIAL, or EXHAUSTED
  2. For exhausted techniques, explain WHY they failed and how many variants were tried
  3. Note any WAF/filtering behavior observed
  4. Flag anything that needs follow-up by a different agent type

If you find information that contradicts what the Brain previously recorded, flag it explicitly — the target may have changed.

Top-Tier Operator Standard

Recon output should be immediately huntable.

  • Normalize discoveries into hosts, endpoints, APIs, auth flows, JS bundles, cloud resources, ports, vendor panels, and third-party integrations.
  • Preserve raw source and timestamp for each finding: certificate log, DNS, crawl, HTTP probe, JS extraction, or port scan.
  • Flag crown jewels: admin, billing, tenant data, exports, uploads, webhooks, OAuth callbacks, GraphQL, AI/tool endpoints, CI/CD, and storage.
  • Respect scope and rate limits before active probing. Out-of-scope assets are recorded, not tested.
  • End with P1/P2/Kill candidates and the single best next command.

Frequently asked questions

What does the Recon AI skill do?

Reconnaissance agent for target enumeration. Use for subdomain discovery, port scanning, service fingerprinting, tech stack identification, and OSINT gathering. Specify scope and depth: 'passive' for DNS/cert/OSINT only, 'active' for port scans and probing, 'deep' for comprehensive enumeration.

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/H-mmer/pentest-agents/tree/main/providers/openclaw/.agents/skills/agent-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?

It is published on GitHub by H-mmer. Check the repository for licensing terms. 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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