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Analyzing Windows Shellbag Artifacts

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mukul975
analyzing-windows-shellbag-artifacts

Analyze Windows Shellbag (BagMRU) registry artifacts with SBECmd and Shellbags Explorer to reconstruct folder browsing activity and prove user interaction with directories, including removable media and network shares, even after the folders are deleted. Use when reconstructing a user's folder access history or proving access to a since-removed directory in DFIR work.

Overview

Publishermukul975
RepositoryAnthropic-Cybersecurity-Skills
Skill nameanalyzing-windows-shellbag-artifacts
Stars
34.3K
Forks
4.2K
Bundled files
6
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Analyzing Windows Shellbag Artifacts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-windows-shellbag-artifacts
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p .claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-windows-shellbag-artifacts .claude/skills/analyzing-windows-shellbag-artifacts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Windows Shellbag Artifacts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyzing Windows Shellbag Artifacts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyzing Windows Shellbag Artifacts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Analyzing Windows Shellbag Artifacts

Overview

Shellbags are Windows registry artifacts that track how users interact with folders through Windows Explorer, storing view settings such as icon size, window position, sort order, and view mode. From a forensic perspective, Shellbags provide definitive evidence of folder access -- even folders that no longer exist on the system. When a user browses to a folder via Windows Explorer, the Open/Save dialog, or the Control Panel, a Shellbag entry is created or updated in the user's registry hive. These entries persist after folder deletion, drive disconnection, and even across user profile resets, making them invaluable for proving that a user navigated to specific directories on local drives, USB devices, network shares, or zip archives.

When to Use

  • When investigating security incidents that require analyzing windows shellbag artifacts
  • 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 digital forensics 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

Registry Locations

Windows 7/8/10/11

HiveKey PathStores
NTUSER.DATSoftware\Microsoft\Windows\Shell\BagMRUFolder hierarchy tree
NTUSER.DATSoftware\Microsoft\Windows\Shell\BagsView settings per folder
UsrClass.datLocal Settings\Software\Microsoft\Windows\Shell\BagMRUDesktop/Explorer shell
UsrClass.datLocal Settings\Software\Microsoft\Windows\Shell\BagsAdditional view settings

BagMRU Structure

The BagMRU key contains a hierarchical tree of numbered subkeys representing the directory structure. Each subkey value contains a Shell Item (SHITEMID) binary blob encoding the folder identity:

  • Root (BagMRU): Desktop namespace root
  • BagMRU\0: Typically "My Computer"
  • BagMRU\0\0: First drive (e.g., C:)
  • BagMRU\0\0\0: First subfolder on C:

Each Shell Item contains:

  • Item type (folder, drive, network, zip, control panel)
  • Short name (8.3 format)
  • Long name (Unicode)
  • Creation/modification timestamps
  • MFT entry/sequence for NTFS folders

Analysis with EZ Tools

SBECmd (Command Line)

powershell
# Parse shellbags from a directory of registry hives
SBECmd.exe -d "C:\Evidence\Registry" --csv C:\Output --csvf shellbags.csv

# Parse from a live system (requires admin)
SBECmd.exe --live --csv C:\Output --csvf live_shellbags.csv

# Key output columns:
# AbsolutePath - Full reconstructed path
# CreatedOn - When the folder was first browsed
# ModifiedOn - When view settings were last changed
# AccessedOn - Last access timestamp
# ShellType - Type of shell item (Directory, Drive, Network, etc.)
# Value - Raw shell item data

ShellBags Explorer (GUI)

powershell
# Launch GUI tool for interactive analysis
ShellBagsExplorer.exe

# Load registry hives: File > Load Hive
# Navigate the tree structure to see folder hierarchy
# Right-click entries for detailed shell item properties

Forensic Investigation Scenarios

Proving USB Device Browsing

text
Shellbag Path: My Computer\E:\Confidential\Project_Files
ShellType: Directory (on removable volume)
CreatedOn: 2025-03-15 09:30:00 UTC

This proves the user navigated to E:\Confidential\Project_Files
via Windows Explorer, even if the USB drive is no longer connected.
The volume letter E: and directory timestamps can be correlated
with USBSTOR and MountPoints2 registry entries.

Detecting Network Share Access

text
Shellbag Path: \\FileServer01\Finance\Q4_Reports
ShellType: Network Location
AccessedOn: 2025-02-20 14:15:00 UTC

This proves the user browsed to a network share, even if
the share has been decommissioned or access revoked.

Identifying Deleted Folder Knowledge

text
Shellbag Path: C:\Users\suspect\Documents\Exfiltration_Staging
ShellType: Directory
CreatedOn: 2025-01-10 08:00:00 UTC

Even though C:\Users\suspect\Documents\Exfiltration_Staging
no longer exists, the Shellbag entry proves the user
created and navigated to this folder.

Limitations

  • Shellbags only record folder-level interactions, not individual file access
  • Only created through Windows Explorer shell and Open/Save dialogs
  • Command-line access (cmd, PowerShell) does not generate Shellbag entries
  • Programmatic file access via APIs does not generate Shellbag entries
  • Timestamps may reflect view setting changes, not necessarily folder access
  • Windows may batch-update Shellbag entries during Explorer shutdown

References

Example Output

text
$ SBECmd.exe -d "C:\Evidence\Users\jsmith" --csv /analysis/shellbag_output

SBECmd v2.1.0 - ShellBags Explorer (Command Line)
====================================================
Processing hives for user: jsmith
  NTUSER.DAT:  C:\Evidence\Users\jsmith\NTUSER.DAT
  UsrClass.dat: C:\Evidence\Users\jsmith\AppData\Local\Microsoft\Windows\UsrClass.dat

[+] NTUSER.DAT shellbag entries:   456
[+] UsrClass.dat shellbag entries: 1,234
[+] Total shellbag entries:        1,690

--- Folder Access Timeline (Incident Window) ---
Last Accessed (UTC)     | Folder Path                                             | Type        | Access Count
------------------------|---------------------------------------------------------|-------------|-------------
2024-01-15 14:34:05     | C:\Users\jsmith\Downloads                               | File System | 45
2024-01-15 14:36:25     | C:\ProgramData\Updates                                  | File System | 3
2024-01-15 15:05:00     | \\FILESERV01\Finance                                    | Network     | 2
2024-01-15 15:12:30     | \\FILESERV01\Finance\Q4_Reports                          | Network     | 1
2024-01-15 15:30:00     | E:\                                                     | Removable   | 4
2024-01-15 15:30:45     | E:\Backup                                               | Removable   | 3
2024-01-15 15:31:20     | E:\Backup\Corporate_Data                                | Removable   | 2
2024-01-15 16:12:45     | \\FILESERV01\HR\Employees                                | Network     | 1
2024-01-15 16:15:00     | \\FILESERV01\HR\Employees\Records_2024                   | Network     | 1
2024-01-16 02:35:00     | C:\Windows\Temp                                         | File System | 5
2024-01-17 02:44:00     | C:\ProgramData\svc                                     | File System | 2
2024-01-18 01:10:00     | C:\Users\jsmith\AppData\Local\Temp                      | File System | 8

--- Network Share Access ---
  \\FILESERV01\Finance             First: 2023-09-10  Last: 2024-01-15
  \\FILESERV01\Finance\Q4_Reports  First: 2024-01-15  Last: 2024-01-15  (NEW)
  \\FILESERV01\HR\Employees        First: 2024-01-15  Last: 2024-01-15  (NEW)
  \\DC01\SYSVOL                    First: 2023-03-15  Last: 2024-01-16  (anomalous access time)

--- Removable Device Access ---
  E:\ (USB Drive)
    Volume Name:    BACKUP_DRIVE
    First Accessed: 2024-01-15 15:30:00 UTC
    Last Accessed:  2024-01-15 15:45:22 UTC
    Folders Browsed: 3 (E:\, E:\Backup, E:\Backup\Corporate_Data)

--- Deleted/No Longer Existing Paths ---
  C:\ProgramData\Updates\                (folder deleted, shellbag persists)
  C:\ProgramData\svc\                    (folder deleted, shellbag persists)
  C:\Windows\Temp\tools\                 (folder deleted, shellbag persists)

Summary:
  Total unique folders accessed:  1,690
  Network shares accessed:        4 (2 newly accessed during incident)
  Removable media:                1 USB device (data staging suspected)
  Deleted folder evidence:        3 paths (anti-forensics indicator)
  CSV exported to:                /analysis/shellbag_output/

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analyzing Windows Shellbag Artifacts AI skill do?

Analyze Windows Shellbag (BagMRU) registry artifacts with SBECmd and Shellbags Explorer to reconstruct folder browsing activity and prove user interaction with directories, including removable media and network shares, even after the folders are deleted. Use when reconstructing a user's folder access history or proving access to a since-removed directory in DFIR work.

Why use Analyzing Windows Shellbag Artifacts on TypingMind?

Because you install it once and use it with any model. Analyzing Windows Shellbag Artifacts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyzing Windows Shellbag Artifacts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-windows-shellbag-artifacts. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analyzing Windows Shellbag Artifacts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyzing Windows Shellbag Artifacts?

As many as you like. As long as a model supports skills, you can use Analyzing Windows Shellbag Artifacts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyzing Windows Shellbag Artifacts AI skill free?

Yes. It is published on GitHub by mukul975 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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