conducting-internal-reconnaissance-with-bloodhound-ce

conducting-internal-reconnaissance-with-bloodhound-ce is a skill for Claude Code from 26zl/cybersec-toolkit. It costs 44 tokens per session (1,938 once invoked), scanned A, original, MIT.

An Active Directory investigation procedure using BloodHound Community Edition, a web-based tool that maps relationships between users, groups, permissions, computers, and trusts.

In plain words
What is it for?
Use it during authorized assessments to collect and visualize Active Directory or Entra ID relationships, find privilege-escalation paths, and identify dangerous permissions or exposed sessions.
Why use it?
It makes hidden routes to higher privileges visible, such as a path from a low-privilege account to Domain Admin access.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Part of the cybersec-toolkit plugin — 197 skills, 2 hooks, 1 MCP server shipped together

Good fit Use it during authorized assessments to collect and visualize Active Directory or Entra ID relationships, find privilege-escalation paths, and identify dangerous permissions or exposed sessions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/26zl/cybersec-toolkit/conducting-internal-reconnaissance-with-bloodhound-ce
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.

Any agent
npx skills add 26zl/cybersec-toolkit --skill conducting-internal-reconnaissance-with-bloodhound-ce
Clone the repo
git clone --depth 1 https://github.com/26zl/cybersec-toolkit

Made for: Claude Code.

Or install cybersec-toolkit, the plugin that ships this one along with the rest of its 197 skills, 2 hooks, 1 MCP server.

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 conducting-internal-reconnaissance-with-bloodhound-ce

README.md
[![agentmods](https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/conducting-internal-reconnaissance-with-bloodhound-ce/github.svg)](https://agentmods.dev/skills/26zl/cybersec-toolkit/conducting-internal-reconnaissance-with-bloodhound-ce)
Your own site
<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/conducting-internal-reconnaissance-with-bloodhound-ce"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/conducting-internal-reconnaissance-with-bloodhound-ce/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 conducting-internal-reconnaissance-with-bloodhound-ce

Your own site · 80×15
<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/conducting-internal-reconnaissance-with-bloodhound-ce"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/conducting-internal-reconnaissance-with-bloodhound-ce.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,938 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high YARA Match · line 2
    YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).
    Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
  • medium Data Exfiltration · line 84
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
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 $0.00044 $0.01938
Opus 5 $0.00022 $0.00969
Sonnet 5 $0.00009 $0.00388
Haiku 4.5 $0.00004 $0.00194

Measured 9d ago against content hash 57600785700d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

conducting-internal-reconnaissance-with-bloodhound-ce scanned grade A with 1 finding 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 9d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/agent.py, scripts/process.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -L https://ghst.ly/getbhce -o docker-compose.yml
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.claude/skills/conducting-internal-reconnaissance-with-bloodhound-ce/SKILL.md · 201 lines

How it starts

The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Conducting Internal Reconnaissance with BloodHound CE

Legal Notice: This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.

Overview

BloodHound Community Edition (CE) is a modern, web-based Active Directory reconnaissance platform developed by SpecterOps that uses graph theory to reveal hidden relationships and attack paths within AD environments. Unlike the legacy BloodHound application, BloodHound CE uses a PostgreSQL backend with a dedicated graph database, providing improved performance, a modern web UI, and enhanced API capabilities. Red teams use BloodHound CE to collect AD objects, ACLs, sessions, group memberships, and trust relationships, then visualize attack paths from compromised low-privileged accounts to high-value targets like Domain Admins. The SharpHound collector (v2 for CE) gathers data from Active Directory, while AzureHound collects from Azure AD / Entra ID environments.

When to Use

  • When conducting security assessments that involve conducting internal reconnaissance with bloodhound ce
  • When following incident response procedures for related security events
  • When performing scheduled security testing or auditing activities
  • When validating security controls through hands-on testing

Prerequisites

  • Familiarity with red teaming concepts and tools
  • Access to a test or lab environment for safe execution
  • Python 3.8+ with required dependencies installed
  • Appropriate authorization for any testing activities

Objectives

  • Deploy BloodHound CE server using Docker Compose
  • Collect AD data using SharpHound v2 or BloodHound.py
  • Import collected data into BloodHound CE for graph analysis
  • Identify shortest attack paths from owned principals to Domain Admins
  • Discover ACL-based attack paths, Kerberoastable accounts, and delegation abuse
  • Execute custom Cypher queries for advanced attack path analysis
  • Generate attack path reports for engagement documentation

Read the full file on GitHub · 201 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 9d ago First seen · 201 lines · 44 tokens per session scan A 57600785700d

Subscribe to this mod's changes

conducting-internal-reconnaissance-with-bloodhound-ce is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 1,938 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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