mcp2agy-recon

mcp2agy-recon is a skill for Claude Code, Codex from uziii2208/mcp2agy. It costs 71 tokens per session (1,196 once invoked), scanned A, original, MIT.

A reconnaissance tool that maps a target’s exposed systems and attack surface. Reconnaissance is the information-gathering stage of a security assessment.

In plain words
What is it for?
Running passive OSINT, discovering ports and services, mapping web and API endpoints, and identifying cloud assets for later security analysis.
Why use it?
It collects information about public data, services, web applications, APIs, and cloud assets before deeper testing begins.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/uziii2208/mcp2agy/recon
Any agent
npx skills add uziii2208/mcp2agy --skill recon
Clone the repo
git clone --depth 1 https://github.com/uziii2208/mcp2agy

Made for: Claude Code, Codex.

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 mcp2agy-recon

README.md
[![agentmods](https://agentmods.dev/badge/skills/uziii2208/mcp2agy/recon.svg)](https://agentmods.dev/skills/uziii2208/mcp2agy/recon)
Your own site
<a href="https://agentmods.dev/skills/uziii2208/mcp2agy/recon"><img src="https://agentmods.dev/badge/skills/uziii2208/mcp2agy/recon.svg" alt="Measured on agentmods" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,196 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00071 $0.01196
Opus 5 $0.00036 $0.00598
Sonnet 5 $0.00014 $0.00239
Haiku 4.5 $0.00007 $0.00120

Measured 4d ago against content hash 73871c58b3f5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

mcp2agy-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 4d 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.

.agents/plugins/mcp2agy/skills/recon/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/recon — Multi-Agent Reconnaissance & Intelligence Engine (v3.0.0)

0. Prime Directive & Orchestration Model

The Reconnaissance engine builds an irrefutable, actionable map of the target's attack surface before exploitation. It coordinates specialized subagents running concurrently to minimize engagement time and maximize surface coverage:

                      ┌─────────────────────────────────┐
                      │   Recon Orchestrator (/recon)   │
                      └────────────────┬────────────────┘
                                       │
        ┌──────────────────┬───────────┴───────────┬──────────────────┐
        ▼                  ▼                       ▼                  ▼
┌──────────────┐   ┌──────────────┐        ┌──────────────┐   ┌──────────────┐
│ Passive OSINT│   │ Active Ports │        │ Web & API Map│   │ Cloud Assets │
│   Subagent   │   │  & Services  │        │   Subagent   │   │   Subagent   │
└──────────────┘   └──────────────┘        └──────────────┘   └──────────────┘
        │                  │                       │                  │
        └──────────────────┴───────────┬───────────┴──────────────────┘
                                       ▼
                     mcp2agy_workspace/recon/results/<target>/
                     ├── target_profile.json
                     ├── attack_surface.md
                     └── active_endpoints.txt ──► [Handoff to /scan or /audit]

1. Multi-Agent Reconnaissance Playbook

Step 1: Initialize Recon Workspace

$target = "target_domain_or_host"
$reconDir = "mcp2agy_workspace/recon/results/$target"
New-Item -ItemType Directory -Force -Path $reconDir | Out-Null

Step 2: Spawn Concurrent Recon Subagents

Use invoke_subagent to spawn specialized recon workers concurrently:

{
  "Subagents": [
    {
      "TypeName": "mcp2agy-recon-agent",
      "Role": "Passive OSINT & DNS Intelligence",
      "Model": "flash",
      "Workspace": "inherit",
      "Prompt": "Perform Passive OSINT & Subdomain Discovery on <target>.\nOutput Dir: mcp2agy_workspace/recon/results/<target>/\nTasks:\n1. Query Certificate Transparency logs (crt.sh), DNS records (A, AAAA, MX, TXT, CNAME).\n2. Enumerate subdomains via subfinder / amass passive.\n3. Check historical endpoints via Wayback Machine.\n4. Write discovered hostnames to mcp2agy_workspace/recon/results/<target>/subdomains.txt."
    },
    {
      "TypeName": "mcp2agy-recon-agent",
      "Role": "Active Service & Port Scanner",
      "Model": "flash",
      "Workspace": "inherit",
      "Prompt": "Perform Active Port & Service Discovery on <target>.\nOutput Dir: mcp2agy_workspace/recon/results/<target>/\nTasks:\n1. Run fast port scan (Top 1000 ports via nmap / masscan).\n2. Probe HTTP/HTTPS services with httpx for title, status, TLS cert, and tech stack fingerprint.\n3. Write active services to mcp2agy_workspace/recon/results/<target>/services.json."
    },
    {
      "TypeName": "mcp2agy-recon-agent",
      "Role": "Cloud Asset & Storage Enumerator",
      "Model": "flash",
      "Workspace": "inherit",
      "Prompt": "Enumerate Cloud Assets & Buckets for <target>.\nOutput Dir: mcp2agy_workspace/recon/results/<target>/\nTasks:\n1. Check for public AWS S3 buckets, Azure Blob containers, and GCP storage buckets named after target.\n2. Check for exposed cloud metadata endpoints / DNS records.\n3. Write findings to mcp2agy_workspace/recon/results/<target>/cloud_assets.json."
    }
  ]
}

Read the full file on GitHub · 100 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. 4d ago First seen · 100 lines · 71 tokens per session scan A 73871c58b3f5

Subscribe to this mod's changes

mcp2agy-recon is a skill published in the GitHub repository uziii2208/mcp2agy (1 stars, last pushed 6d ago), licensed MIT. It adds 71 tokens to every session and 1,196 once invoked, about $0.0004 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 2026-08-31.

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