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Add folder anatomy (scripts/agent.py + references/api-reference.md) for 648 cybersecurity skills
Complete skill folder anatomy across all cybersecurity skills: - scripts/agent.py: 80-150 line Python agents using real libraries (impacket, boto3, azure-mgmt-*, kubernetes, pefile, yara, scapy, shodan, stix2, etc.) - references/api-reference.md: real API documentation with method signatures - LICENSE: MIT license for all skill folders
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name: analyzing-memory-forensics-with-lime-and-volatility
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description: >
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Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module
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and analysis with Volatility 3 framework. Extracts process lists, network connections,
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bash history, loaded kernel modules, and injected code from Linux memory images.
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Use when performing incident response on compromised Linux systems.
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---
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# Analyzing Memory Forensics with LiME and Volatility
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## Instructions
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Acquire Linux memory using LiME kernel module, then analyze with Volatility 3
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to extract forensic artifacts from the memory image.
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```bash
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# LiME acquisition
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insmod lime-$(uname -r).ko "path=/evidence/memory.lime format=lime"
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# Volatility 3 analysis
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vol3 -f /evidence/memory.lime linux.pslist
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vol3 -f /evidence/memory.lime linux.bash
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vol3 -f /evidence/memory.lime linux.sockstat
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```
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```python
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import volatility3
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from volatility3.framework import contexts, automagic
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from volatility3.plugins.linux import pslist, bash, sockstat
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# Programmatic Volatility 3 usage
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context = contexts.Context()
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automagics = automagic.available(context)
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```
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Key analysis steps:
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1. Acquire memory with LiME (format=lime or format=raw)
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2. List processes with linux.pslist, compare with linux.psscan
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3. Extract bash command history with linux.bash
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4. List network connections with linux.sockstat
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5. Check loaded kernel modules with linux.lsmod for rootkits
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## Examples
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```bash
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# Full forensic workflow
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vol3 -f memory.lime linux.pslist | grep -v "\[kthread\]"
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vol3 -f memory.lime linux.bash
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vol3 -f memory.lime linux.malfind
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vol3 -f memory.lime linux.lsmod
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```
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