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 memory-forensics-volatilitygit 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/memory-forensics-volatility)<a href="https://agentmods.dev/skills/yaklang/hack-skills/memory-forensics-volatility"><img src="https://agentmods.dev/badge/skills/yaklang/hack-skills/memory-forensics-volatility/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/memory-forensics-volatility"><img src="https://agentmods.dev/badge/skills/yaklang/hack-skills/memory-forensics-volatility.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk fail
- 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 161 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.
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 | $0.00044 | $0.02814 |
| Opus 5 | $0.00022 | $0.01407 |
| Sonnet 5 | $0.00009 | $0.00563 |
| Haiku 4.5 | $0.00004 | $0.00281 |
Grade A, and why
memory-forensics-volatility 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 6d 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
3 near-identical copies found in the catalogue:
- memory-forensics-volatility — 100% identical, 0 lines differ
- memory-forensics-volatility — 100% identical, 0 lines differ
- memory-forensics-volatility — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: Memory Forensics — Expert Analysis Playbook
AI LOAD INSTRUCTION: Expert memory forensics techniques using Volatility 2 and 3. Covers memory acquisition, OS identification, process analysis (hidden process detection), network connections, DLL/module analysis, code injection detection (malfind), credential extraction, file carving, registry analysis, and timeline generation. Base models miss the Vol2/Vol3 command differences, malware indicator patterns, and Linux-specific memory analysis.
0. RELATED ROUTING
Before going deep, consider loading:
- traffic-analysis-pcap for correlating network artifacts with memory findings
- steganography-techniques if hidden data suspected in extracted files
- windows-privilege-escalation for understanding post-exploitation artifacts in memory
Quick Reference
Also load VOLATILITY_CHEATSHEET.md when you need:
- Vol2 vs Vol3 command comparison table
- Common plugin sequences for specific investigation types
1. MEMORY ACQUISITION
Linux
# LiME (Linux Memory Extractor) — kernel module
insmod lime.ko "path=/tmp/mem.lime format=lime"
# /proc/kcore (if available)
dd if=/proc/kcore of=/tmp/mem.raw bs=1M
# AVML (Microsoft's open-source)
./avml /tmp/mem.lime
Windows
# WinPmem
winpmem_mini_x64.exe memdump.raw
# FTK Imager (GUI) — capture memory to file
# DumpIt (single-click memory dump)
DumpIt.exe
# Comae (MagnetRAM)
MagnetRAMCapture.exe /output memdump.raw
Virtual Machines
# VMware: .vmem file in VM directory (suspend VM first)
# VirtualBox: VBoxManage debugvm "VM_NAME" dumpvmcore --filename mem.raw
# KVM/QEMU: virsh dump DOMAIN memdump --memory-only
# Hyper-V: checkpoint VM → inspect .bin files
2. VOLATILITY 2 vs 3
| Concept | Volatility 2 | Volatility 3 |
|---|---|---|
| Profile system | --profile=Win10x64_19041 |
Auto-detected (symbol tables) |
| Image info | imageinfo |
windows.info / linux.info |
| Process list | pslist |
windows.pslist |
| Network | netscan / connections |
windows.netscan / windows.netstat |
| DLLs | dlllist |
windows.dlllist |
| Injection | malfind |
windows.malfind |
| Hashes | hashdump |
windows.hashdump |
| Files | filescan |
windows.filescan |
| Registry | hivelist / printkey |
windows.registry.hivelist / windows.registry.printkey |
| Install | pip2 install volatility |
pip3 install volatility3 |
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.
- 6d ago First seen · 324 lines · 44 tokens per session scan A 1a31f124ee8e
memory-forensics-volatility is a skill published in the GitHub repository yaklang/hack-skills (2,143 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 2,814 once invoked, about $0.0002 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 2026-09-03.
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