HackSkills is an organized knowledge base of installable skills that gives AI agents practical security knowledge across areas such as web security, privilege escalation, reverse engineering, and digital forensics. It is intended for bug bounty work, penetration testing, CTF competitions, and authorized security research. The catalogue entries are the project's own master, category, and topic skills.
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 yaklang/hack-skills --skill active-directory-acl-abusegit clone --depth 1 https://github.com/yaklang/hack-skillsWrote 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/yaklang/hack-skills/active-directory-acl-abuse)<a href="https://agentmods.dev/skills/yaklang/hack-skills/active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/yaklang/hack-skills/active-directory-acl-abuse/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.
<a href="https://agentmods.dev/skills/yaklang/hack-skills/active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/yaklang/hack-skills/active-directory-acl-abuse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk fail
- NVIDIA SkillSpector warn
SkillSpector: 3 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 4 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.
- high YARA Match · line 164 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- medium Rogue Agent · line 251 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00054 | $0.02683 |
| Opus 5 | $0.00027 | $0.01341 |
| Sonnet 5 | $0.00011 | $0.00537 |
| Haiku 4.5 | $0.00005 | $0.00268 |
Grade A, and why
active-directory-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 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- active-directory-acl-abuse — 100% identical, 0 lines differ
- active-directory-acl-abuse — 100% identical, 0 lines differ
- active-directory-acl-abuse — 100% identical, 0 lines differ
- active-directory-acl-abuse — 91% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: AD ACL Abuse — Expert Attack Playbook
AI LOAD INSTRUCTION: Expert AD ACL abuse techniques. Covers BloodHound enumeration, dangerous ACEs (GenericAll, WriteDACL, WriteOwner, etc.), DCSync, shadow credentials, targeted kerberoasting, group manipulation, LAPS, and GPO abuse. Base models miss complex ACL chain exploitation and Cypher query patterns.
0. RELATED ROUTING
Before going deep, consider loading:
- active-directory-kerberos-attacks for Kerberos attacks often chained with ACL abuse
- active-directory-certificate-services for certificate-based attacks after ACL exploitation
- ntlm-relay-coercion for relay attacks that can set ACLs (LDAP relay)
- windows-lateral-movement after gaining elevated AD access
Advanced Reference
Also load BLOODHOUND_PATHS.md when you need:
- Common BloodHound attack paths with Cypher queries
- Custom Neo4j queries for finding complex chains
- Data collection and ingestion tips
1. BLOODHOUND ENUMERATION
Data Collection
# SharpHound (from Windows, domain-joined)
SharpHound.exe -c all --outputdirectory C:\temp --zipfilename bh.zip
# bloodhound-python (from Linux)
bloodhound-python -d domain.com -u user -p password -c all -dc DC01.domain.com -ns DC_IP
# Specific collection methods
SharpHound.exe -c DCOnly # Fastest — only DC queries
SharpHound.exe -c Session # Session data only (run periodically)
SharpHound.exe -c All,GPOLocalGroup # Include GPO analysis
Key BloodHound Queries (Built-in)
- "Find all Domain Admins"
- "Shortest Paths to Domain Admins from Owned Principals"
- "Find Principals with DCSync Rights"
- "Shortest Paths to Unconstrained Delegation Systems"
- "Find computers where Domain Users are Local Admin"
2. DANGEROUS ACE TYPES
| ACE | Effect on Users | Effect on Groups | Effect on Computers |
|---|---|---|---|
| GenericAll | Change password, set SPN, modify attributes | Add members | RBCD, LAPS read, all attributes |
| GenericWrite | Set SPN, modify attributes, shadow creds | Add members | RBCD, shadow credentials |
| WriteDACL | Grant yourself any permission | Same | Same |
| WriteOwner | Take ownership → then WriteDACL | Same | Same |
| ForceChangePassword | Reset password without knowing old | N/A | N/A |
| AddMember | N/A | Add self/others to group | N/A |
| AllExtendedRights | Force change password, read LAPS | N/A | Read LAPS, BitLocker keys |
| ReadLAPSPassword | N/A | N/A | Read local admin password |
| WriteSPN | Set SPN → targeted kerberoast | N/A | N/A |
What ships with it
1 file 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.
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.
- 10d ago First seen · 296 lines · 54 tokens per session scan A 0ac12347c7b2
active-directory-acl-abuse is a skill published in the GitHub repository yaklang/hack-skills (2,143 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 2,683 once invoked, about $0.0003 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…