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 adriannoes/awesome-agentic-ai --skill performing-memory-forensics-with-volatility3git clone --depth 1 https://github.com/adriannoes/awesome-agentic-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/adriannoes/awesome-agentic-ai/performing-memory-forensics-with-volatility3)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/performing-memory-forensics-with-volatility3"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/performing-memory-forensics-with-volatility3/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/adriannoes/awesome-agentic-ai/performing-memory-forensics-with-volatility3"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/performing-memory-forensics-with-volatility3.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 | $0.00036 | $0.03194 |
| Opus 5 | $0.00018 | $0.01597 |
| Sonnet 5 | $0.00007 | $0.00639 |
| Haiku 4.5 | $0.00004 | $0.00319 |
Grade D, and why
performing-memory-forensics-with-volatility3 scanned grade D with 3 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Harvests environment variableshighData exfiltration
Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.
- When you need to extract credentials, encryption keys, or network connections from memory Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo insmod lime-$(uname -r).ko "path=/cases/memory/linux_mem.lime format=lime" Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://downloads.volatilityfoundation.org/volatility3/symbols/windows.zip How it starts
The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performing Memory Forensics with Volatility 3
When to Use
- When analyzing a RAM dump from a compromised or suspect system
- During incident response to identify running malware, injected code, or rootkits
- When you need to extract credentials, encryption keys, or network connections from memory
- For detecting process hollowing, DLL injection, or hidden processes
- When disk-based forensics alone is insufficient and volatile data is critical
Prerequisites
- Python 3.7+ installed
- Volatility 3 framework installed (
pip install volatility3) - Memory dump in raw, ELF, or crash dump format
- Appropriate symbol tables (ISF files) for the target OS version
- Sufficient disk space for analysis output (2-3x memory dump size)
- Optional: YARA rules for malware scanning in memory
Workflow
Step 1: Acquire Memory Dump and Install Volatility 3
# Install Volatility 3
pip install volatility3
# Or install from source for latest features
git clone https://github.com/volatilityfoundation/volatility3.git
cd volatility3
pip install -e .
# Download Windows symbol tables (ISF packs)
# Place in volatility3/symbols/ directory
wget https://downloads.volatilityfoundation.org/volatility3/symbols/windows.zip
unzip windows.zip -d /opt/volatility3/volatility3/symbols/
# Download Linux and Mac symbol packs
wget https://downloads.volatilityfoundation.org/volatility3/symbols/linux.zip
wget https://downloads.volatilityfoundation.org/volatility3/symbols/mac.zip
# Memory acquisition tools (for live systems):
# Windows: winpmem, DumpIt, FTK Imager
# Linux: LiME (Linux Memory Extractor)
sudo insmod lime-$(uname -r).ko "path=/cases/memory/linux_mem.lime format=lime"
# Verify the memory dump
file /cases/case-2024-001/memory/memory.raw
ls -lh /cases/case-2024-001/memory/memory.raw
Step 2: Identify the Operating System Profile
# Run banners plugin to identify the OS
vol -f /cases/case-2024-001/memory/memory.raw banners
# For Windows, identify the OS version
vol -f /cases/case-2024-001/memory/memory.raw windows.info
# Output example:
# Variable Value
# Kernel Base 0xf8047e200000
# DTB 0x1ad000
# Symbols ntkrnlmp.pdb/GUID
# Is64Bit True
# IsPAE False
# primary layer Intel32e
# KdVersionBlock 0xf8047ee232c0
# Major/Minor 15.19041
# Machine Type 34404
# KeNumberProcessors 4
# SystemTime 2024-01-18 14:32:15 UTC
# NtBuildLab 19041.1.amd64fre.vb_release.191206-1406
# NtProductType NtProductWinNt
# NtSystemRoot C:\WINDOWS
# PE MajorOperatingSystemVersion 10
# PE MinorOperatingSystemVersion 0
# For Linux memory dumps
vol -f /cases/case-2024-001/memory/linux_mem.lime linux.info
What ships with it
4 files 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.
- 7d ago First seen · 303 lines · 36 tokens per session scan D 61d7e7fb4d0d
performing-memory-forensics-with-volatility3 is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 13d ago), licensed MIT. It adds 36 tokens to every session and 3,194 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it D with 3 findings (harvests environment variables, asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
performing-memory-forensics-with-volatility3
Use when analyze volatile memory dumps using Volatility 3 to extract running processes, network connections, loaded modules, and evidence of malicious activity. Use when analyzeing volatile memory dumps using volatility 3 to extract running.
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…
analyzing-memory-dumps-with-volatility
A procedure for examining a captured computer's RAM with Volatility, a memory-forensics tool. It helps investigate activity that may exist only in memory, such as hidden processes, injected code, network connections, or credentials.
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…