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Analyzing Windows Lnk Files For Artifacts

Organization
costrict-plugins-repo
analyzing-windows-lnk-files-for-artifacts

Parse Windows LNK shortcut files to extract target paths, MAC timestamps, volume serial numbers, and machine identifiers for forensic timeline reconstruction. Use when investigating recently-accessed files, tracking removable media or network paths referenced by shortcuts, or building a DFIR timeline from LNK artifacts.

Overview

Publishercostrict-plugins-repo
Repositorymukul975-anthropic-cybersecurity-skills-cybersecurity-skills
Skill nameanalyzing-windows-lnk-files-for-artifacts
Stars
75
Forks
13
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by costrict-plugins-repo on GitHub. Read the source before you install it.

Installation

Install the Analyzing Windows Lnk Files For 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/costrict-plugins-repo/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills/tree/main/skills/analyzing-windows-lnk-files-for-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/costrict-plugins-repo/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills.git /tmp/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills
mkdir -p .claude/skills
cp -r /tmp/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills/skills/analyzing-windows-lnk-files-for-artifacts .claude/skills/analyzing-windows-lnk-files-for-artifacts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Windows Lnk Files For 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 Lnk Files For 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 Lnk Files For 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 LNK Files for Artifacts

When to Use

  • When reconstructing user file access history from Windows shortcut files
  • For tracking accessed files, network shares, and removable media
  • During investigations to prove a user opened specific documents
  • When correlating file access with other timeline artifacts
  • For identifying accessed paths on remote systems or USB devices

Prerequisites

  • Access to LNK files from forensic image (Recent, Desktop, Quick Launch)
  • LECmd (Eric Zimmerman), python-lnk, or LnkParser for analysis
  • Understanding of LNK file structure (Shell Link Binary format)
  • Knowledge of LNK file locations on Windows systems
  • Forensic workstation with analysis tools installed

Workflow

Step 1: Collect LNK Files from Forensic Image

bash
# Mount forensic image
mount -o ro,loop,offset=$((2048*512)) /cases/case-2024-001/images/evidence.dd /mnt/evidence

mkdir -p /cases/case-2024-001/lnk/{recent,desktop,startup,custom}

# Copy Recent items LNK files (primary source)
cp /mnt/evidence/Users/*/AppData/Roaming/Microsoft/Windows/Recent/*.lnk \
   /cases/case-2024-001/lnk/recent/ 2>/dev/null

# Copy automatic destinations (Jump Lists)
cp /mnt/evidence/Users/*/AppData/Roaming/Microsoft/Windows/Recent/AutomaticDestinations/*.automaticDestinations-ms \
   /cases/case-2024-001/lnk/recent/ 2>/dev/null

# Copy custom destinations (pinned Jump List items)
cp /mnt/evidence/Users/*/AppData/Roaming/Microsoft/Windows/Recent/CustomDestinations/*.customDestinations-ms \
   /cases/case-2024-001/lnk/custom/ 2>/dev/null

# Copy Desktop shortcuts
cp /mnt/evidence/Users/*/Desktop/*.lnk /cases/case-2024-001/lnk/desktop/ 2>/dev/null

# Copy Startup folder shortcuts (persistence)
cp /mnt/evidence/Users/*/AppData/Roaming/Microsoft/Windows/Start\ Menu/Programs/Startup/*.lnk \
   /cases/case-2024-001/lnk/startup/ 2>/dev/null
cp "/mnt/evidence/ProgramData/Microsoft/Windows/Start Menu/Programs/Startup"/*.lnk \
   /cases/case-2024-001/lnk/startup/ 2>/dev/null

# Find all LNK files on the system
find /mnt/evidence/ -name "*.lnk" -type f 2>/dev/null > /cases/case-2024-001/lnk/all_lnk_locations.txt

# Count and hash
ls /cases/case-2024-001/lnk/recent/ | wc -l
sha256sum /cases/case-2024-001/lnk/recent/*.lnk > /cases/case-2024-001/lnk/lnk_hashes.txt 2>/dev/null

Step 2: Parse LNK Files with LECmd

bash
# Using Eric Zimmerman's LECmd (Windows or via Mono)
# Process all LNK files in a directory
LECmd.exe -d "C:\cases\lnk\recent\" --csv "C:\cases\analysis\" --csvf lnk_analysis.csv

# Process a single LNK file with verbose output
LECmd.exe -f "C:\cases\lnk\recent\document.pdf.lnk"

# Process Jump List files
JLECmd.exe -d "C:\cases\lnk\recent\" --csv "C:\cases\analysis\" --csvf jumplist_analysis.csv

# Output includes:
# - Source file path
# - Target path (file that was accessed)
# - Target creation, modification, access timestamps
# - LNK creation and modification timestamps
# - Working directory
# - Command line arguments
# - Volume serial number and label
# - Drive type (Fixed, Removable, Network)
# - Machine ID (NetBIOS name)
# - MAC address (from tracker database)
# - File size of target

Step 3: Parse LNK Files with Python

bash
pip install LnkParse3

python3 << 'PYEOF'
import LnkParse3
import os, json, csv
from datetime import datetime

lnk_dir = '/cases/case-2024-001/lnk/recent/'
results = []

for filename in sorted(os.listdir(lnk_dir)):
    if not filename.lower().endswith('.lnk'):
        continue

    filepath = os.path.join(lnk_dir, filename)
    try:
        with open(filepath, 'rb') as f:
            lnk = LnkParse3.lnk_file(f)
            info = lnk.get_json()

            parsed = {
                'lnk_file': filename,
                'target_path': '',
                'working_dir': '',
                'arguments': '',
                'target_created': '',
                'target_modified': '',
                'target_accessed': '',
                'file_size': '',
                'drive_type': '',
                'volume_serial': '',
                'volume_label': '',
                'machine_id': '',
                'mac_address': '',
            }

            # Extract header timestamps
            header = info.get('header', {})
            parsed['target_created'] = str(header.get('creation_time', ''))
            parsed['target_modified'] = str(header.get('modified_time', ''))
            parsed['target_accessed'] = str(header.get('accessed_time', ''))
            parsed['file_size'] = str(header.get('file_size', ''))

            # Extract link info
            link_info = info.get('link_info', {})
            if link_info:
                local_path = link_info.get('local_base_path', '')
                network_path = link_info.get('common_network_relative_link', {}).get('net_name', '')
                parsed['target_path'] = local_path or network_path

                vol_info = link_info.get('volume_id', {})
                if vol_info:
                    parsed['drive_type'] = str(vol_info.get('drive_type', ''))
                    parsed['volume_serial'] = str(vol_info.get('drive_serial_number', ''))
                    parsed['volume_label'] = str(vol_info.get('volume_label', ''))

            # Extract string data
            string_data = info.get('string_data', {})
            parsed['working_dir'] = str(string_data.get('working_dir', ''))
            parsed['arguments'] = str(string_data.get('command_line_arguments', ''))

            # Extract tracker data (machine ID and MAC)
            extra = info.get('extra', {})
            tracker = extra.get('DISTRIBUTED_LINK_TRACKER_BLOCK', {})
            if tracker:
                parsed['machine_id'] = str(tracker.get('machine_id', ''))
                parsed['mac_address'] = str(tracker.get('mac_address', ''))

            results.append(parsed)

            # Print summary
            print(f"\n{filename}")
            print(f"  Target: {parsed['target_path']}")
            print(f"  Modified: {parsed['target_modified']}")
            print(f"  Drive: {parsed['drive_type']} (Serial: {parsed['volume_serial']})")
            if parsed['machine_id']:
                print(f"  Machine: {parsed['machine_id']}")

    except Exception as e:
        print(f"  Error parsing {filename}: {e}")

# Write results to CSV
with open('/cases/case-2024-001/analysis/lnk_analysis.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=results[0].keys() if results else [])
    writer.writeheader()
    writer.writerows(results)

print(f"\n\nTotal LNK files parsed: {len(results)}")
PYEOF

Step 4: Analyze for Investigative Value

bash
# Identify files accessed from removable media
python3 << 'PYEOF'
import csv

with open('/cases/case-2024-001/analysis/lnk_analysis.csv') as f:
    reader = csv.DictReader(f)

    print("=== FILES ACCESSED FROM REMOVABLE MEDIA ===\n")
    removable = []
    network = []

    for row in reader:
        if 'DRIVE_REMOVABLE' in row.get('drive_type', '').upper() or \
           'removable' in row.get('drive_type', '').lower():
            removable.append(row)
            print(f"  {row['target_modified']} | {row['target_path']} | Vol: {row['volume_serial']}")

        if 'network' in row.get('drive_type', '').lower() or \
           row.get('target_path', '').startswith('\\\\'):
            network.append(row)

    print(f"\n=== FILES ACCESSED FROM NETWORK SHARES ===\n")
    for row in network:
        print(f"  {row['target_modified']} | {row['target_path']}")

    print(f"\nRemovable media files: {len(removable)}")
    print(f"Network share files: {len(network)}")

    # Check for unique machines (tracker data)
    machines = set()
    for row in [*removable, *network]:
        if row.get('machine_id'):
            machines.add(row['machine_id'])
    if machines:
        print(f"\nMachine IDs found: {machines}")
PYEOF

# Check Startup folder LNK files for persistence
echo "=== STARTUP FOLDER SHORTCUTS (PERSISTENCE) ===" > /cases/case-2024-001/analysis/startup_persistence.txt
for lnk in /cases/case-2024-001/lnk/startup/*.lnk; do
    python3 -c "
import LnkParse3
with open('$lnk', 'rb') as f:
    lnk = LnkParse3.lnk_file(f)
    info = lnk.get_json()
    target = info.get('link_info', {}).get('local_base_path', 'Unknown')
    args = info.get('string_data', {}).get('command_line_arguments', '')
    print(f'  $(basename $lnk): {target} {args}')
" >> /cases/case-2024-001/analysis/startup_persistence.txt 2>/dev/null
done

Key Concepts

ConceptDescription
Shell Link (.lnk)Windows shortcut file format containing target path, timestamps, and metadata
Target timestampsCreation, modification, and access times of the file the shortcut points to
Volume serial numberUnique identifier of the drive volume where the target file resides
Machine IDNetBIOS name embedded by the Distributed Link Tracking service
MAC addressNetwork adapter MAC from the machine that created the LNK file
Jump ListsRecent and pinned file lists per application (contain embedded LNK data)
Automatic DestinationsSystem-managed Jump List entries for recently opened files
Custom DestinationsUser-pinned Jump List items that persist until manually removed

Tools & Systems

ToolPurpose
LECmdEric Zimmerman command-line LNK file parser with CSV/JSON output
JLECmdEric Zimmerman Jump List parser
LnkParse3Python library for programmatic LNK file analysis
lnk_parserAlternative Python LNK parsing tool
AutopsyForensic platform with LNK file analysis module
KAPEAutomated LNK and Jump List artifact collection
PlasoTimeline tool with LNK file parser for super-timeline creation
LNK ExplorerGUI tool for interactive LNK file examination

Common Scenarios

Scenario 1: Data Exfiltration via USB Drive Analyze Recent folder LNK files for targets on removable drives, correlate volume serial numbers with USBSTOR registry entries, build a list of files accessed from USB devices, establish which documents were opened from the removable drive, correlate with file copy timestamps.

Scenario 2: Malware Persistence via Startup Shortcuts Examine Startup folder LNK files for malicious targets, check target path and arguments for encoded commands or suspicious executables, verify target file exists and examine it, correlate creation timestamp with initial compromise time.

Scenario 3: Network Share Access Investigation Filter LNK files with network paths (UNC targets), identify which network shares were accessed and when, correlate machine IDs with known corporate systems, check if sensitive file servers were accessed outside of normal duties, build access timeline for compliance investigation.

Scenario 4: Document Access Timeline for Legal Proceedings Extract all Recent folder LNK files, build chronological list of documents accessed by the user, identify specific files relevant to the case, present target timestamps showing when files were opened, correlate with email and communication timelines.

Output Format

LNK File Analysis Summary:
  User Profile: suspect_user
  Total LNK Files: 234 (Recent: 198, Desktop: 23, Startup: 5, Other: 8)

  File Access Statistics:
    Local drive (C:):    156 files
    Removable media:     23 files (3 unique volume serials)
    Network shares:      15 files (\\server01, \\fileserver)
    Other drives:        4 files

  Machine IDs Found: DESKTOP-ABC123, LAPTOP-XYZ789
  MAC Addresses: AA:BB:CC:DD:EE:FF, 11:22:33:44:55:66

  Removable Media Access:
    Volume Serial 1234-ABCD:
      2024-01-15 14:32 - E:\Confidential\financial_report.xlsx
      2024-01-15 14:45 - E:\Confidential\customer_database.csv
      2024-01-15 15:00 - E:\Projects\source_code.zip

  Startup Persistence:
    updater.lnk -> C:\ProgramData\svc\updater.exe (SUSPICIOUS)
    OneDrive.lnk -> C:\Users\...\OneDrive.exe (Legitimate)

  Timeline: /cases/case-2024-001/analysis/lnk_analysis.csv

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 Lnk Files For Artifacts AI skill do?

Parse Windows LNK shortcut files to extract target paths, MAC timestamps, volume serial numbers, and machine identifiers for forensic timeline reconstruction. Use when investigating recently-accessed files, tracking removable media or network paths referenced by shortcuts, or building a DFIR timeline from LNK artifacts.

Why use Analyzing Windows Lnk Files For Artifacts on TypingMind?

Because you install it once and use it with any model. Analyzing Windows Lnk Files For 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 Lnk Files For Artifacts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/costrict-plugins-repo/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills/tree/main/skills/analyzing-windows-lnk-files-for-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 Lnk Files For 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 Lnk Files For Artifacts?

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

Is the Analyzing Windows Lnk Files For Artifacts AI skill free?

Yes. It is published on GitHub by costrict-plugins-repo 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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