How do I install the Analyzing Memory Forensics With Lime And Volatility skill?

Run npx skillmds add thedixitjain/analyzing-memory-forensics-with-lime-and-volatility in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Which AI agents work with Analyzing Memory Forensics With Lime And Volatility?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Analyzing Memory Forensics With Lime And Volatility free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Analyzing Memory Forensics With Lime And Volatility?

thedixitjain (@thedixitjain) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Analyzing Memory Forensics With Lime And Volatility by thedixitjain

Name: Analyzing Memory Forensics With Lime And Volatility
Rating: 4.666666666666667 (3 reviews)
Author: thedixitjain

Analyzing Memory Forensics With Lime And Volatility

'Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems. '

thedixitjain 6d06f44 4 files · 22.2 KB Updated Sep 9, 2026 2 repo stars

File contents

Analyzing Memory Forensics with LiME and Volatility

When to Use

When investigating security incidents that require analyzing memory forensics with lime and volatility
When building detection rules or threat hunting queries for this domain
When SOC analysts need structured procedures for this analysis type
When validating security monitoring coverage for related attack techniques

Prerequisites

Familiarity with security operations concepts and tools
Access to a test or lab environment for safe execution
Python 3.8+ with required dependencies installed
Appropriate authorization for any testing activities

Instructions

Acquire Linux memory using LiME kernel module, then analyze with Volatility 3 to extract forensic artifacts from the memory image.

# LiME acquisition
insmod lime-$(uname -r).ko "path=/evidence/memory.lime format=lime"

# Volatility 3 analysis
vol3 -f /evidence/memory.lime linux.pslist
vol3 -f /evidence/memory.lime linux.bash
vol3 -f /evidence/memory.lime linux.sockstat

import volatility3
from volatility3.framework import contexts, automagic
from volatility3.plugins.linux import pslist, bash, sockstat

# Programmatic Volatility 3 usage
context = contexts.Context()
automagics = automagic.available(context)

Key analysis steps:

Acquire memory with LiME (format=lime or format=raw)
List processes with linux.pslist, compare with linux.psscan
Extract bash command history with linux.bash
List network connections with linux.sockstat
Check loaded kernel modules with linux.lsmod for rootkits

Examples

# Full forensic workflow
vol3 -f memory.lime linux.pslist | grep -v "\[kthread\]"
vol3 -f memory.lime linux.bash
vol3 -f memory.lime linux.malfind
vol3 -f memory.lime linux.lsmod

Source: mukul975/Anthropic-Cybersecurity-Skills → skills/analyzing-memory-forensics-with-lime-and-volatility/SKILL.md

---name: analyzing-memory-forensics-with-lime-and-volatilitydescription: 'Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems. '--- # Analyzing Memory Forensics with LiME and Volatility ## When to Use - When investigating security incidents that require analyzing memory forensics with lime and volatility - When building detection rules or threat hunting queries for this domain - When SOC analysts need structured procedures for this analysis type - When validating security monitoring coverage for related attack techniques ## Prerequisites - Familiarity with security operations concepts and tools - Access to a test or lab environment for safe execution - Python 3.8+ with required dependencies installed - Appropriate authorization for any testing activities ## Instructions Acquire Linux memory using LiME kernel module, then analyze with Volatility 3 to extract forensic artifacts from the memory image. ```bash # LiME acquisition insmod lime-$(uname -r).ko "path=/evidence/memory.lime format=lime" # Volatility 3 analysis vol3 -f /evidence/memory.lime linux.pslist vol3 -f /evidence/memory.lime linux.bash vol3 -f /evidence/memory.lime linux.sockstat ``` ```python import volatility3 from volatility3.framework import contexts, automagic from volatility3.plugins.linux import pslist, bash, sockstat # Programmatic Volatility 3 usage context = contexts.Context() automagics = automagic.available(context) ``` Key analysis steps: 1. Acquire memory with LiME (format=lime or format=raw) 2. List processes with linux.pslist, compare with linux.psscan 3. Extract bash command history with linux.bash 4. List network connections with linux.sockstat 5. Check loaded kernel modules with linux.lsmod for rootkits ## Examples ```bash # Full forensic workflow vol3 -f memory.lime linux.pslist | grep -v "\[kthread\]" vol3 -f memory.lime linux.bash vol3 -f memory.lime linux.malfind vol3 -f memory.lime linux.lsmod ``` --- **Source:** [`mukul975/Anthropic-Cybersecurity-Skills`](https://github.com/mukul975/Anthropic-Cybersecurity-Skills) → `skills/analyzing-memory-forensics-with-lime-and-volatility/SKILL.md`

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