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 SecurityTalent/bugskill-ai --skill memory-forensics-volatilitygit clone --depth 1 https://github.com/SecurityTalent/bugskill-aiWrote 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/securitytalent/bugskill-ai/memory-forensics-volatility)<a href="https://agentmods.dev/skills/securitytalent/bugskill-ai/memory-forensics-volatility"><img src="https://agentmods.dev/badge/skills/securitytalent/bugskill-ai/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/securitytalent/bugskill-ai/memory-forensics-volatility"><img src="https://agentmods.dev/badge/skills/securitytalent/bugskill-ai/memory-forensics-volatility.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.5 | $0.00018 | $0.01126 |
| Sonnet 5.5 | $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 1mo 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.
This is a copy
100% identical to memory-forensics-volatility — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 1mo ago First seen · 324 lines · 44 tokens per session scan A 1a31f124ee8e
memory-forensics-volatility is a skill published in the GitHub repository SecurityTalent/bugskill-ai (13 stars, last pushed 21d ago), licensed MIT. It adds 44 tokens to every session and 2,814 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to memory-forensics-volatility, differing in 0 lines, and is treated as a copy.
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