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analyzing-memory-forensics-with-lime-and-volatility

'Performs Linux memory acquisition using LiME (Linux Memory Extractor)

Install / Use

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-memory-forensics-with-lime-and-volatility

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

90/100

Category

Security

Supported Platforms

Universal

Our assessment of analyzing-memory-forensics-with-lime-and-volatility

analyzing-memory-forensics-with-lime-and-volatility scores 90/100 on our quality scale, 251st of 544 Security skills we index (top 47%).

Its SKILL.md is 2.5 KB long, well organised into 9 sections with 3 code examples: a solid amount of guidance for an agent.

With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
18/20
Description
12/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 25 days ago, so analyzing-memory-forensics-with-lime-and-volatility is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-26. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

analyzing-memory-forensics-with-lime-and-volatility compared with similar skills

All 4 of these similar skills score higher than analyzing-memory-forensics-with-lime-and-volatility; compare them before choosing.

SkillScoreStarsUpdatedFormat
analyzing-memory-forensics-with-lime-and-volatility (this skill)by mukul9759033.3k25d agoSKILL.md
LocalAIby mudler10049.3ktodayMCP Server
algorithmic-artby anthropics100177.9k3d agoSKILL.md
pptxby anthropics100177.9k3d agoSKILL.md
designby nextlevelbuilder100130.2k4d agoSKILL.md

Frequently asked questions

How do I install analyzing-memory-forensics-with-lime-and-volatility?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-memory-forensics-with-lime-and-volatility. The install tabs above show the steps for each supported agent.
Which AI agents does analyzing-memory-forensics-with-lime-and-volatility work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is analyzing-memory-forensics-with-lime-and-volatility safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is analyzing-memory-forensics-with-lime-and-volatility still maintained?
The repository was last updated 25 days ago, so analyzing-memory-forensics-with-lime-and-volatility is actively maintained.

name: analyzing-memory-forensics-with-lime-and-volatility description: '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.

' domain: cybersecurity subdomain: security-operations tags:

  • memory-forensics
  • linux-forensics
  • lime
  • volatility
  • incident-response
  • kernel-modules version: '1.0' author: mahipal license: Apache-2.0 nist_csf:
  • DE.CM-01
  • RS.MA-01
  • GV.OV-01
  • DE.AE-02 mitre_attack:
  • T1055
  • T1003.001
  • T1620
  • T1564.001

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:

  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

# 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

Related Skills

View on GitHub
GitHub Stars33.3k
CategorySecurity
Updated25d ago
Forks4.0k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions