AD Methodology

AD Methodology is an AI Engineering skill for Claude Code, published by ADScanPro in Claude-AD.

176 stars28 forkson ADScanPro/Claude-ADAdded 2026/09/08+5% in starsRepository updated 2026/08/24

active-directoryactive-directory-securityadcsbloodhoundclaudeclaude-codeclaude-code-pluginclaude-skillsdcsynckerberoastingkerberosntlm-relayoffensive-securitypenetration-testingpentestingred-team

Install in seconds

Install AD Methodology

Copy AD Methodology into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/ADScanPro/Claude-AD/tree/main/skills/ad-methodology ~/.claude/skills/ad-methodology

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/ADScanPro/Claude-AD.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/ad-methodology/SKILL.md in ADScanPro/Claude-AD
Installs to: ~/.claude/skills/ad-methodology
Collection: One of 8 skills cataloged from this repository
Category: AI Engineering — 3670 skills

What AD Methodology does

AD Methodology gives the phase order for an Active Directory assessment: setup, collection, exploitation, and post-processing. Use it to decide what to run next, avoid lockouts, and collect the right data before attacking.

AD Methodology is cataloged under AI Engineering on DirSkills. AD Methodology comes from a repository tagged active-directory, active-directory-security, adcs, bloodhound and claude.

Documentation

README

Excerpt

AD Pentest Methodology: Phase Order

A domain assessment is not a bag of tricks you run in random order. The order is the craft. Enumeration feeds exploitation; a credential harvested cheaply saves you a spray that locks accounts; a graph collected once tells you which of a hundred possible attacks actually reaches Domain Admin. Run the phases in order and each one narrows the next.

Four phases, in sequence:

Setup: reachability, name resolution, environment posture, first credentials.
Collection: topology, trusts, directory objects, hosts, shares. Read, do not touch.
Exploitation: attack-path discovery, then cheap wins, then spraying, then hunting.
Post-processing: consolidate loot, re-collect as the owned set grows, report.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about AD Methodology

What else does ADScanPro publish alongside AD Methodology?
AD Methodology is one of 8 skills that DirSkills catalogs from ADScanPro/Claude-AD, the repository it ships in. Its siblings there include AD CS Attacks, AD Environment Constraints and AD OPSEC Telemetry. Each one is a separate skill with its own page in this directory, installs the same way AD Methodology does, and is maintained by ADScanPro in that same repository. The rest of the collection is listed on the ADScanPro/Claude-AD page.
How does AD Methodology compare to other AI Engineering skills?
AD Methodology ranks #3143 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of AD Methodology against them. Open each page to compare what they document and how they install.