Oss Forensics
Tommy-yw/RunbookHermes
Supply chain investigation, evidence recovery, and forensic analysis for GitHub repositories.
Agent skill
Parse Windows LNK shortcut files to extract target paths, MAC timestamps, volume serial numbers, and machine identifiers for forensic timeline reconstruction.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-windows-lnk-files-for-artifacts --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "analyzing-windows-lnk-files-for-artifacts" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-windows-lnk-files-for-artifacts into .claude/skills/analyzing-windows-lnk-files-for-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-windows-lnk-files-for-artifacts", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-windows-lnk-files-for-artifactsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-windows-lnk-files-for-artifacts --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/analyzing-windows-lnk-files-for-artifacts .agents/skills/analyzing-windows-lnk-files-for-artifacts && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analyzing-windows-lnk-files-for-artifacts" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-windows-lnk-files-for-artifacts into .agents/skills/analyzing-windows-lnk-files-for-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-windows-lnk-files-for-artifacts", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-windows-lnk-files-for-artifacts --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/analyzing-windows-lnk-files-for-artifacts .cursor/skills/analyzing-windows-lnk-files-for-artifacts && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analyzing-windows-lnk-files-for-artifacts" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-windows-lnk-files-for-artifacts into .cursor/skills/analyzing-windows-lnk-files-for-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-windows-lnk-files-for-artifacts", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git --path skills/analyzing-windows-lnk-files-for-artifacts--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-windows-lnk-files-for-artifacts --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/analyzing-windows-lnk-files-for-artifacts .gemini/skills/analyzing-windows-lnk-files-for-artifacts && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analyzing-windows-lnk-files-for-artifacts" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-windows-lnk-files-for-artifacts into .gemini/skills/analyzing-windows-lnk-files-for-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-windows-lnk-files-for-artifacts", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-windows-lnk-files-for-artifactsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/analyzing-windows-lnk-files-for-artifacts .github/skills/analyzing-windows-lnk-files-for-artifacts && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analyzing-windows-lnk-files-for-artifacts" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-windows-lnk-files-for-artifacts into .github/skills/analyzing-windows-lnk-files-for-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-windows-lnk-files-for-artifacts", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-lnk-files-for-artifacts -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-windows-lnk-files-for-artifacts --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/analyzing-windows-lnk-files-for-artifacts .opencode/skills/analyzing-windows-lnk-files-for-artifacts && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analyzing-windows-lnk-files-for-artifacts" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-windows-lnk-files-for-artifacts into .opencode/skills/analyzing-windows-lnk-files-for-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-windows-lnk-files-for-artifacts", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
analyzing-windows-lnk-files-for-artifactsParse 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. 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.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3pipFrom the folder's file list and the shell code blocks in SKILL.md.
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.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 481 words, ~3,204 tokens.
.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.# 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# 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 targetpip 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# 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| Concept | Description |
|---|---|
| Shell Link (.lnk) | Windows shortcut file format containing target path, timestamps, and metadata |
| Target timestamps | Creation, modification, and access times of the file the shortcut points to |
| Volume serial number | Unique identifier of the drive volume where the target file resides |
| Machine ID | NetBIOS name embedded by the Distributed Link Tracking service |
| MAC address | Network adapter MAC from the machine that created the LNK file |
| Jump Lists | Recent and pinned file lists per application (contain embedded LNK data) |
| Automatic Destinations | System-managed Jump List entries for recently opened files |
| Custom Destinations | User-pinned Jump List items that persist until manually removed |
| Tool | Purpose |
|---|---|
| LECmd | Eric Zimmerman command-line LNK file parser with CSV/JSON output |
| JLECmd | Eric Zimmerman Jump List parser |
| LnkParse3 | Python library for programmatic LNK file analysis |
| lnk_parser | Alternative Python LNK parsing tool |
| Autopsy | Forensic platform with LNK file analysis module |
| KAPE | Automated LNK and Jump List artifact collection |
| Plaso | Timeline tool with LNK file parser for super-timeline creation |
| LNK Explorer | GUI tool for interactive LNK file examination |
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.
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
SKILL.md and 3 other files (scripts, references) in skills/analyzing-windows-lnk-files-for-artifacts of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analyzing Windows Lnk Files For Artifacts this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.2k | Automated safety check: Pass | Apache-2.0 | |
| Oss ForensicsTommy-yw/RunbookHermes | 546 | 3 repos | ~5k | Automated safety check: Pass | MIT | |
| Ctf Malwareljagiello/ctf-skills | 3.4k | — | ~2.1k | Automated safety check: Notes | MIT | |
| Dfirtransilienceai/communitytools | 563 | — | ~1.5k | Automated safety check: Pass | MIT | |
| TShark Traffic AnalysisAgentSecOps/SecOpsAgentKit | 220 | 1 repos | ~4.8k | Automated safety check: Notes | Custom licence | |
| Runtime Memory Sample Acquisitiondslsdzc/rev-skills | 135 | — | ~2k | Automated safety check: Pass | Apache-2.0 |
Tommy-yw/RunbookHermes
Supply chain investigation, evidence recovery, and forensic analysis for GitHub repositories.
ljagiello/ctf-skills
Provides malware analysis and network traffic techniques for CTF challenges.
transilienceai/communitytools
Digital forensics and incident response - Windows event log analysis, PCAP forensics, filesystem artifact analysis, AD attack detection, and timeline correlation.
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.
dslsdzc/rev-skills
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.
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.
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.
mukul975/Anthropic-Cybersecurity-Skills
Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.
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.
mukul975/Anthropic-Cybersecurity-Skills
Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.
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.
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.
Categories
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.