agentmods.dev

engage.recon

engage.recon is a command for Claude Code from hypnguyen1209/offensive-claude. It costs 12 tokens per session (994 once invoked), scanned A, original, MIT.

A command for the reconnaissance phase of a security engagement. Reconnaissance means collecting information about an authorized target and mapping the systems that could be exposed to attack.

In plain words
What is it for?
Use it to enumerate subdomains, inspect certificate and DNS records, gather public company information, and create a reconnaissance plan for a web, network, cloud, or other security assessment.
Why use it?
It organizes passive research and, when authorized, active scanning into an attack-surface map. It can also record useful techniques from earlier engagements.

Command for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python skills/engagement-memory/scripts/pattern_db.py match \.

Part of the offensive-claude plugin — 30 skills, 18 commands, 8 agents, 1 hook shipped together

not rated 357repo +3 24d ago A scan 12 tokens original MIT

Good fit Use it to enumerate subdomains, inspect certificate and DNS records, gather public company information, and create a reconnaissance plan for a web, network, cloud, or other security assessment.

Compare 6 commands from other repositories ↓
View source ↗ hypnguyen1209/offensive-claude
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/hypnguyen1209/offensive-claude
agentmods
npx agentmods add commands/hypnguyen1209/offensive-claude/engage.recon

Made for: Claude Code.

Or install offensive-claude, the plugin that ships this one along with the rest of its 30 skills, 18 commands, 8 agents, 1 hook.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for engage.recon

README.md
[![agentmods](https://agentmods.dev/badge/commands/hypnguyen1209/offensive-claude/engage.recon/github.svg)](https://agentmods.dev/commands/hypnguyen1209/offensive-claude/engage.recon)
Your own site
<a href="https://agentmods.dev/commands/hypnguyen1209/offensive-claude/engage.recon"><img src="https://agentmods.dev/badge/commands/hypnguyen1209/offensive-claude/engage.recon/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for engage.recon

Your own site · 80×15
<a href="https://agentmods.dev/commands/hypnguyen1209/offensive-claude/engage.recon"><img src="https://agentmods.dev/badge/commands/hypnguyen1209/offensive-claude/engage.recon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 12 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 994 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00012 $0.00994
Opus 5 $0.00006 $0.00497
Sonnet 5 $0.00002 $0.00199
Haiku 4.5 $0.00001 $0.00099

Measured 10d ago against content hash 2f22498d68ca, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

engage.recon scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/engage.recon.md · 148 lines

How it starts

The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/engage.recon

Executes Phase 1 (Reconnaissance) of the engagement workflow.

Usage

/engage.recon [--passive-only] [--active]

Options:

  • --passive-only: Only passive reconnaissance (no direct target interaction)
  • --active: Include active scanning (default if authorized in scope)

Process

1. Load Templates

Loads:

  • recon/recon-plan.md — Reconnaissance methodology
  • recon/attack-surface.md — Attack surface map template

1.5 Prior-Intel Recall (engagement-memory)

Query the cross-engagement pattern memory for techniques that already worked against this target's class / tech stack, and write them to .engage/recon/prior-intel.md so weaponization starts from proven TTPs instead of re-deriving them:

python skills/engagement-memory/scripts/pattern_db.py match \
    --target <host> --tech-stack <stack> --top 10 --json > .engage/recon/prior-intel.json

2. Passive Reconnaissance

Executes in order:

Subdomain Enumeration:

  • Certificate transparency logs (crt.sh)
  • DNS enumeration (subfinder, amass)
  • Search engine dorking
  • Historical DNS records

OSINT Gathering:

  • WHOIS information
  • Company information (LinkedIn, Crunchbase)
  • Email addresses and naming conventions
  • Technology stack identification (BuiltWith, Wappalyzer)
  • GitHub/GitLab repository discovery
  • Pastebin/leak searches

Infrastructure Mapping:

  • ASN and IP range identification
  • Cloud provider detection (AWS/Azure/GCP)
  • CDN and WAF detection

3. Active Reconnaissance

If authorized:

Port Scanning:

  • Full TCP port scan on discovered hosts
  • Service version detection
  • OS fingerprinting

Service Enumeration:

  • HTTP/HTTPS service discovery
  • Banner grabbing
  • SSL/TLS configuration analysis

Web Application Fingerprinting:

  • CMS detection (WordPress, Drupal, etc.)
  • Framework identification
  • JavaScript library analysis
  • API endpoint discovery

4. Attack Surface Mapping

Populates recon/attack-surface.md with:

  • Discovered subdomains and hosts
  • Open ports and services
  • Web applications and entry points
  • Identified technologies and versions
  • Potential attack vectors
  • High-value targets

Read the full file on GitHub · 148 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 148 lines · 12 tokens per session scan A 2f22498d68ca

Subscribe to this mod's changes

engage.recon is a command published in the GitHub repository hypnguyen1209/offensive-claude (357 stars, last pushed 24d ago), licensed MIT. It adds 12 tokens to every session and 994 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Claude CodeSecurityclaude-code-skillsexploit-developmentoffensive-securityredteam
Related

Other commands, from other repositories

discover

Run a full product discovery cycle — from outcome definition through opportunity mapping, prioritisation, and experiment design. Use when the team isn't sure what to build next, or before writing a PRD for a complex feature space.

avelikiy/great_cto · 44 tokens

voice-compliance

Voice/telephony compliance check — invokes voice-ai-reviewer to produce TM-voice-{slug}.md with TCPA, STIR/SHAKEN, state recording-consent, EU AI Act Art. 50, and synth-voice deepfake-law gaps.

avelikiy/great_cto · 53 tokens

claude-tracker

List recent Claude Code sessions with live status.

tdimino/claude-code-minoan · 0 tokens

qa

Smoke or browser-walk a running app. Report only. Do not implement. Do not merge.

Kripu77/software-factory · 22 tokens

esp-harden

Harden and inspect ESP32 firmware for field failures, crashes, memory, and security.

mathisk2095/jko-claude-plugins · 17 tokens

replication-package

Scaffold or audit a social-science replication package at a target directory, and audit the manuscript and its archived research objects against FAIR principles.

scdenney/open-science-skills · 0 tokens