memory-forensics

wshobson/agents · updated Apr 8, 2026

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$npx skills add https://github.com/wshobson/agents --skill memory-forensics
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summary

Acquire, analyze, and extract artifacts from memory dumps for incident response and malware analysis.

  • Supports live memory acquisition across Windows (WinPmem, DumpIt), Linux (LiME, /dev/mem), and macOS (osxpmem), plus virtual machine memory from VMware, VirtualBox, QEMU, and Hyper-V
  • Volatility 3 framework with 30+ plugins covering process analysis, network connections, DLL inspection, code injection detection, registry analysis, and file system artifacts
  • Includes malware analysis an
skill.md

Memory Forensics

Comprehensive techniques for acquiring, analyzing, and extracting artifacts from memory dumps for incident response and malware analysis.

Memory Acquisition

Live Acquisition Tools

Windows

# WinPmem (Recommended)
winpmem_mini_x64.exe memory.raw

# DumpIt
DumpIt.exe

# Belkasoft RAM Capturer
# GUI-based, outputs raw format

# Magnet RAM Capture
# GUI-based, outputs raw format

Linux

# LiME (Linux Memory Extractor)
sudo insmod lime.ko "path=/tmp/memory.lime format=lime"

# /dev/mem (limited, requires permissions)
sudo dd if=/dev/mem of=memory.raw bs=1M

# /proc/kcore (ELF format)
sudo cp /proc/kcore memory.elf

macOS

# osxpmem
sudo ./osxpmem -o memory.raw

# MacQuisition (commercial)

Virtual Machine Memory

# VMware: .vmem file is raw memory
cp vm.vmem memory.raw

# VirtualBox: Use debug console
vboxmanage debugvm "VMName" dumpvmcore --filename memory.elf

# QEMU
virsh dump <domain> memory.raw --memory-only

# Hyper-V
# Checkpoint contains memory state

Volatility 3 Framework

Installation and Setup

# Install Volatility 3
pip install volatility3

# Install symbol tables (Windows)
# Download from https://downloads.volatilityfoundation.org/volatility3/symbols/

# Basic usage
vol -f memory.raw <plugin>

# With symbol path
vol -f memory.raw -s /path/to/symbols windows.pslist

Essential Plugins

Process Analysis

# List processes
vol -f memory.raw windows.pslist

# Process tree (parent-child relationships)
vol -f memory.raw windows.pstree

# Hidden process detection
vol -f memory.raw windows.psscan

# Process memory dumps
vol -f memory.raw windows.memmap --pid <PID> --dump

# Process environment variables
vol -f memory.raw windows.envars --pid <PID>

# Command line arguments
vol -f memory.raw windows.cmdline

Network Analysis

# Network connections
vol -f memory.raw windows.netscan

# Network connection state
vol -f memory.raw windows.netstat

DLL and Module Analysis

# Loaded DLLs per process
vol -f memory.raw windows.dlllist --pid <PID>

# Find hidden/injected DLLs
vol -f memory.raw windows.ldrmodules

# Kernel modules
vol -f memory.raw windows.modules

# Module dumps
vol -f memory.raw windows.moddump --pid <PID>

Memory Injection Detection

# Detect code injection
vol -f memory.raw windows.malfind

# VAD (Virtual Address Descriptor) analysis
vol -f memory.raw windows.vadinfo --pid <PID>

# Dump suspicious memory regions
vol -f memory.raw windows.vadyarascan --yara-rules rules.yar

Registry Analysis

# List registry hives
vol -f memory.raw windows.registry.hivelist

# Print registry key
vol -f memory.raw windows.registry.printkey --key "Software\Microsoft\Windows\CurrentVersion\Run"

# Dump registry hive
vol -f memory.raw windows.registry.hivescan --dump

File System Artifacts

# Scan for file objects
vol -f memory.raw windows.filescan

# Dump files from memory
vol -f memory.raw windows.dumpfiles --pid <PID>

# MFT analysis
vol -f memory.raw windows.mftscan

Linux Analysis

# Process listing
vol -f memory.raw linux.pslist

# Process tree
vol -f memory.raw linux.pstree

# Bash history
vol -f memory.raw linux.bash

# Network connections
vol -f memory.raw linux.sockstat

# Loaded kernel modules
vol -f memory.raw linux.lsmod

# Mount points
vol -f memory.raw linux.mount

# Environment variables
vol -f memory.raw linux.envars

macOS Analysis

# Process listing
vol -f memory.raw mac.pslist

# Process tree
vol -f memory.raw mac.pstree

# Network connections
vol -f memory.raw mac.netstat

# Kernel extensions
vol -f memory.raw mac.lsmod

Analysis Workflows

Malware Analysis Workflow

# 1. Initial process survey
vol -f memory.raw windows.pstree > processes.txt
vol -f memory.raw windows.pslist > pslist.txt

# 2. Network connections
vol -f memory.raw windows.netscan > network.txt

# 3. Detect injection
vol -f memory.raw windows.malfind > malfind.txt

# 4. Analyze suspicious processes
vol -f memory.raw windows.dlllist --pid <PID>
vol -f memory.raw windows.handles --pid <PID>

# 5. Dump suspicious executables
vol -f memory.raw windows.pslist --pid <PID> --dump

# 6. Extract strings from dumps
strings -a pid.<PID>.exe > strings.txt

# 7. YARA scanning
vol -f memory.raw windows.yarascan --yara-rules malware.yar

Incident Response Workflow

# 1. Timeline of events
vol -f memory.raw windows.timeliner > timeline.csv

# 2. User activity
vol -f memory.raw windows.cmdline
vol -f memory.raw windows.consoles

# 3. Persistence mechanisms
vol -f memory.raw windows.registry.printkey \
    --key "Software\Microsoft\Windows\CurrentVersion\Run"

# 4. Services
vol -f memory.raw windows.svcscan

# 5. Scheduled tasks
vol -f memory.raw windows.scheduled_tasks

# 6. Recent files
vol -f memory.raw windows.filescan | grep -i "recent"

Data Structures

Windows Process Structures

// EPROCESS (Executive Process)
typedef struct _EPROCESS {
    KPROCESS Pcb;                    // Kernel process block
    EX_PUSH_LOCK ProcessLock;
    LARGE_INTEGER CreateTime;
    LARGE_INTEGER ExitTime;
    // ...
    LIST_ENTRY ActiveProcessLinks;   // Doubly-linked list
    ULONG_PTR UniqueProcessId;       // PID
    // ...
    PEB* Peb;                        // Process Environment Block
    // ...
} EPROCESS;

// PEB (Process Environment Block)
typedef struct _PEB {
    BOOLEAN InheritedAddressSpace;
    BOOLEAN ReadImageFileExecOptions;
    BOOLEAN BeingDebugged;           // Anti-debug check
    // ...
    PVOID ImageBaseAddress;          // Base address of executable
    PPEB_LDR_DATA Ldr;              // Loader data (DLL list)
    PRTL_USER_PROCESS_PARAMETERS ProcessParameters;
    // ...
} PEB;

VAD (Virtual Address Descriptor)

typedef struct _MMVAD {
    MMVAD_SHORT Core;
    union {
        ULONG LongFlags;
        MMVAD_FLAGS VadFlags;
    } u;
    // ...
    PVOID FirstPrototypePte;
    PVOID LastContiguousPte;
    // ...
    PFILE_OBJECT FileObject;
} MMVAD;

// Memory protection flags
#define 
how to use memory-forensics

How to use memory-forensics on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add memory-forensics
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/wshobson/agents --skill memory-forensics

The skills CLI fetches memory-forensics from GitHub repository wshobson/agents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/memory-forensics

Reload or restart Cursor to activate memory-forensics. Access the skill through slash commands (e.g., /memory-forensics) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.755 reviews
  • Noor Bansal· Dec 16, 2024

    Useful defaults in memory-forensics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ama Park· Dec 16, 2024

    memory-forensics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Arya Perez· Dec 8, 2024

    I recommend memory-forensics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Harper Rahman· Dec 8, 2024

    memory-forensics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Omar Rahman· Nov 27, 2024

    memory-forensics reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Sofia Thompson· Nov 15, 2024

    memory-forensics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Naina Thompson· Nov 15, 2024

    We added memory-forensics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Naina Garcia· Nov 7, 2024

    Registry listing for memory-forensics matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aarav Kim· Nov 7, 2024

    Solid pick for teams standardizing on skills: memory-forensics is focused, and the summary matches what you get after install.

  • Aanya Sethi· Oct 26, 2024

    memory-forensics reduced setup friction for our internal harness; good balance of opinion and flexibility.

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