Agent skill

Analyzing Windows Lnk Files For Artifacts

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Parse Windows LNK shortcut files to extract target paths, MAC timestamps, volume serial numbers, and machine identifiers for forensic timeline reconstruction.

Apache-2.0Auto-check passedSecurity

Install Analyzing Windows Lnk Files For Artifacts

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-windows-lnk-files-for-artifacts --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyzing-windows-lnk-files-for-artifacts .claude/skills/analyzing-windows-lnk-files-for-artifacts && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
analyzing-windows-lnk-files-for-artifacts
GitHub stars
34k
Token cost
~3.2k tokens
SKILL.md length
481 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Parse Windows LNK shortcut files to extract target paths, MAC timestamps, volume serial numbers, and machine identifiers for forensic timeline reconstruction.

  • Works in 4 steps: Collect LNK Files from Forensic Image → Parse LNK Files with LECmd → Parse LNK Files with Python → …
  • Investigating recently-accessed files
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3 and pip

What it does

Analyzing Windows Lnk Files For Artifacts is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. 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.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security, covering Digital forensics. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Investigating recently-accessed files
  • Tracking removable media
  • Network paths referenced by shortcuts
  • Building a DFIR timeline from LNK artifacts

Example prompts

  • “/analyzing-windows-lnk-files-for-artifacts”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Collect LNK Files from Forensic Image
  2. Parse LNK Files with LECmd
  3. Parse LNK Files with Python
  4. Analyze for Investigative Value

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Analyzing Windows Lnk Files For Artifacts loads about 3.2k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 481 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~91
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 481 words, ~3,204 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-windows-lnk-files-for-artifacts/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-windows-lnk-files-for-artifacts
description
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.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, lnk-files, windows-artifacts, shortcut-analysis, timeline-reconstruction, evidence-collection
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1547.001, T1204.002, T1005, T1025, T1074.001

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
Show full SKILL.md (182 more words)Show less

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

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/analyzing-windows-lnk-files-for-artifacts of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

Analyzing Windows Lnk Files For Artifacts next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Analyzing Windows Lnk Files For Artifacts compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Analyzing Windows Lnk Files For Artifacts this skillmukul975/Anthropic-Cybersecurity-Skills34k—~3.2kAutomated safety check: PassApache-2.0
Oss ForensicsTommy-yw/RunbookHermes5463 repos~5kAutomated safety check: PassMIT
Ctf Malwareljagiello/ctf-skills3.4k—~2.1kAutomated safety check: NotesMIT
Dfirtransilienceai/communitytools563—~1.5kAutomated safety check: PassMIT
TShark Traffic AnalysisAgentSecOps/SecOpsAgentKit2201 repos~4.8kAutomated safety check: NotesCustom licence
Runtime Memory Sample Acquisitiondslsdzc/rev-skills135—~2kAutomated safety check: PassApache-2.0

Similar skills

  • Oss Forensics

    Tommy-yw/RunbookHermes

    Supply chain investigation, evidence recovery, and forensic analysis for GitHub repositories.

    546 GitHub starsUsed in 3 repos~5k tokens
    SecurityAuto-check passed
  • Ctf Malware

    ljagiello/ctf-skills

    Provides malware analysis and network traffic techniques for CTF challenges.

    3.4k GitHub stars~2.1k tokensUpdated 27 days ago
    SecurityAuto-check: notes
  • Dfir

    transilienceai/communitytools

    Digital forensics and incident response - Windows event log analysis, PCAP forensics, filesystem artifact analysis, AD attack detection, and timeline correlation.

    563 GitHub stars~1.5k tokensUpdated 2 mo ago
    SecurityAuto-check passed
  • TShark Traffic Analysis

    AgentSecOps/SecOpsAgentKit

    Guides authorized packet capture and analysis with TShark, Wireshark's command-line tool, for security investigations, malware detection and forensic examination of network traffic.

    220 GitHub starsUsed in 1 repo~4.8k tokens
    SecurityAuto-check: notes
  • Captures an analyzable sample from a live system when the target leaves no file on disk, by finding abnormal executable memory and the execution context that reached it.

    135 GitHub stars~2k tokensUpdated 5 days ago
    SecurityAuto-check passed
  • Digital Forensics

    zhaoxuya520/reverse-skill

    A skill your agent uses for authorized digital forensics including memory dumps, disk timelines, PCAP investigation, artifact triage, and IR evidence preservation.

    41k GitHub starsUsed in 2 repos~389 tokens
    SecurityAuto-check: warnings

More from mukul975/Anthropic-Cybersecurity-Skills

All 644 skills in this repo
  • Campaign Attribution Evidence Analysis

    mukul975/Anthropic-Cybersecurity-Skills

    Weighs infrastructure, TTP, malware code and timing evidence with the Diamond Model and competing hypotheses to reach a confidence-rated attribution.

    34k GitHub stars~2.3k tokensUpdated 1 mo ago
    Auto-check passed
  • Go Malware Analysis in Ghidra

    mukul975/Anthropic-Cybersecurity-Skills

    Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.

    34k GitHub stars~2.8k tokensUpdated 1 mo ago
    Auto-check passed
  • LNK and Jump List Forensics

    mukul975/Anthropic-Cybersecurity-Skills

    Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.

    34k GitHub stars~2.8k tokensUpdated 1 mo ago
    Auto-check passed
  • Malware Persistence Analysis with Autoruns

    mukul975/Anthropic-Cybersecurity-Skills

    Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.

    34k GitHub stars~1.2k tokensUpdated 1 mo ago
    Auto-check passed
  • NTFS MFT Deleted File Recovery

    mukul975/Anthropic-Cybersecurity-Skills

    Guides a Windows forensic examination of the NTFS Master File Table to recover deleted-file evidence, build timelines and spot timestomping.

    34k GitHub stars~2.7k tokensUpdated 1 mo ago
    Auto-check passed
  • Network Covert Channel Analysis

    mukul975/Anthropic-Cybersecurity-Skills

    Detects DNS tunneling, ICMP exfiltration and HTTP-based covert channels in packet captures and DNS logs when hunting for hidden command-and-control traffic.

    34k GitHub stars~2k tokensUpdated 1 mo ago
    Auto-check passed

Categories

Questions about Analyzing Windows Lnk Files For Artifacts

What does Analyzing Windows Lnk Files For Artifacts do?

Parse Windows LNK shortcut files to extract target paths, MAC timestamps, volume serial numbers, and machine identifiers for forensic timeline reconstruction. Analyzing Windows Lnk Files For Artifacts is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Parse Windows LNK shortcut files to extract target paths, MAC timestamps, volume serial numbers, and machine identifiers for forensic timeline reconstruction.

When should I use Analyzing Windows Lnk Files For Artifacts?

Analyzing Windows Lnk Files For Artifacts fits situations like: investigating recently-accessed files; tracking removable media; network paths referenced by shortcuts; building a DFIR timeline from LNK artifacts.

How do I install Analyzing Windows Lnk Files For Artifacts in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a claude-code`. Or copy the skill folder (skills/analyzing-windows-lnk-files-for-artifacts in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-windows-lnk-files-for-artifacts in your project. Claude Code loads it when a task matches its description.

How do I install Analyzing Windows Lnk Files For Artifacts in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a codex`. Or copy the skill folder (skills/analyzing-windows-lnk-files-for-artifacts in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-windows-lnk-files-for-artifacts in your project. Codex loads it when a task matches its description.

Can I use Analyzing Windows Lnk Files For Artifacts in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-windows-lnk-files-for-artifacts, .gemini/skills/analyzing-windows-lnk-files-for-artifacts, .github/skills/analyzing-windows-lnk-files-for-artifacts and .opencode/skills/analyzing-windows-lnk-files-for-artifacts in your project.

What does Analyzing Windows Lnk Files For Artifacts need to run?

Going by SKILL.md and its folder, Analyzing Windows Lnk Files For Artifacts needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and pip). Our summary lists: Python 3.

Does Analyzing Windows Lnk Files For Artifacts access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Analyzing Windows Lnk Files For Artifacts safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Analyzing Windows Lnk Files For Artifacts use?

Analyzing Windows Lnk Files For Artifacts is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analyzing Windows Lnk Files For Artifacts use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 576 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Windows Lnk Files For Artifacts?

Skills that share tags, products or a category with Analyzing Windows Lnk Files For Artifacts: Oss Forensics (Tommy-yw/RunbookHermes, 546 stars), Ctf Malware (ljagiello/ctf-skills, 3.4k stars), Dfir (transilienceai/communitytools, 563 stars) and TShark Traffic Analysis (AgentSecOps/SecOpsAgentKit, 220 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Windows Lnk Files For Artifacts?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.