Mmcp.market

analyzing-windows-registry-for-artifacts skill

by mukul975·mukul975/Anthropic-Cybersecurity-Skills·34k stars·Apache-2.0

Extract and analyze Windows Registry hives with tools like RegRipper

A100/100content scan

Is the analyzing-windows-registry-for-artifacts skill safe?

Clean: nothing in its files matched our rules. We read 4 files in the folder on 2026-09-28.

No findings.

Install the analyzing-windows-registry-for-artifacts skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

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-registry-for-artifacts ~/.claude/skills/analyzing-windows-registry-for-artifacts
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Analyzing Windows Registry for Artifacts

When to Use

  • When investigating user activity on a Windows system during an incident
  • For identifying autorun/persistence mechanisms used by malware
  • When tracing installed software, USB devices, and network connections
  • During insider threat investigations to reconstruct user actions
  • For correlating registry timestamps with other forensic artifacts

Prerequisites

  • Forensic image or extracted registry hive files
  • RegRipper, Registry Explorer (Eric Zimmerman), or python-registry
  • Access to registry hive locations (SAM, SYSTEM, SOFTWARE, NTUSER.DAT, UsrClass.dat)
  • Understanding of Windows Registry structure (hives, keys, values)
  • SIFT Workstation or forensic analysis environment

Workflow

Step 1: Extract Registry Hives from the Forensic Image

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

# Copy system registry hives
cp /mnt/evidence/Windows/System32/config/SAM /cases/case-2024-001/registry/
cp /mnt/evidence/Windows/System32/config/SYSTEM /cases/case-2024-001/registry/
cp /mnt/evidence/Windows/System32/config/SOFTWARE /cases/case-2024-001/registry/
cp /mnt/evidence/Windows/System32/config/SECURITY /cases/case-2024-001/registry/
cp /mnt/evidence/Windows/System32/config/DEFAULT /cases/case-2024-001/registry/

# Copy user-specific hives
cp /mnt/evidence/Users/*/NTUSER.DAT /cases/case-2024-001/registry/
cp /mnt/evidence/Users/*/AppData/Local/Microsoft/Windows/UsrClass.dat /cases/case-2024-001/registry/

# Copy transaction logs (for dirty hive recovery)
cp /mnt/evidence/Windows/System32/config/*.LOG* /cases/case-2024-001/registry/logs/

# Hash all extracted hives
sha256sum /cases/case-2024-001/registry/* > /cases/case-2024-001/registry/hive_hashes.txt

Step 2: Analyze with RegRipper for Automated Artifact Extraction

# Install RegRipper
git clone https://github.com/keydet89/RegRipper3.0.git /opt/regripper

# Run RegRipper against NTUSER.DAT (user profile)
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/NTUSER.DAT \
   -f ntuser > /cases/case-2024-001/analysis/ntuser_report.txt

# Run against SYSTEM hive
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -f system > /cases/case-2024-001/analysis/system_report.txt

# Run against SOFTWARE hive
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SOFTWARE \
   -f software > /cases/case-2024-001/analysis/software_report.txt

# Run against SAM hive (user accounts)
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SAM \
   -f sam > /cases/case-2024-001/analysis/sam_report.txt

# Run specific plugins
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/NTUSER.DAT \
   -p userassist > /cases/case-2024-001/analysis/userassist.txt

perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p usbstor > /cases/case-2024-001/analysis/usbstor.txt

Step 3: Extract Persistence and Autorun Entries

# Using python-registry for targeted extraction
pip install python-registry

python3 << 'PYEOF'
from Registry import Registry

# Open SOFTWARE hive
reg = Registry.Registry("/cases/case-2024-001/registry/SOFTWARE")

# Check Run keys (autostart)
autorun_paths = [
    "Microsoft\\Windows\\CurrentVersion\\Run",
    "Microsoft\\Windows\\CurrentVersion\\RunOnce",
    "Microsoft\\Windows\\CurrentVersion\\RunServices",
    "Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\Run",
    "Wow6432Node\\Microsoft\\Windows\\CurrentVersion\\Run"
]

for path in autorun_paths:
    try:
        key = reg.open(path)
        print(f"\n=== {path} (Last Modified: {key.timestamp()}) ===")
        for value in key.values():
            print(f"  {value.name()}: {value.value()}")
    except Registry.RegistryKeyNotFoundException:
        pass

# Check installed services
key = reg.open("Microsoft\\Windows NT\\CurrentVersion\\Svchost")
print(f"\n=== Svchost Groups ===")
for value in key.values():
    print(f"  {value.name()}: {value.value()}")
PYEOF

# Check NTUSER.DAT for user-specific autorun
python3 << 'PYEOF'
from Registry import Registry

reg = Registry.Registry("/cases/case-2024-001/registry/NTUSER.

Step 4: Analyze User Activity Artifacts

# Extract UserAssist data (program execution history with ROT13 encoding)
python3 << 'PYEOF'
from Registry import Registry
import codecs, struct, datetime

reg = Registry.Registry("/cases/case-2024-001/registry/NTUSER.DAT")

ua_path = "Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\UserAssist"
key = reg.open(ua_path)

for guid_key in key.subkeys():
    count_key = guid_key.subkey("Count")
    print(f"\n=== {guid_key.name()} ===")
    for value in count_key.values():
        decoded_name = codecs.decode(value.name(), 'rot_13')
        data = value.value()
        if len(data) >= 16:
            run_count = struct.unpack('<I', data[4:8])[0]
            focus_count = struct.unpack('<I', data[8:12])[0]
            timestamp = struct.unpack('<Q', data[60:68])[0] if len(data) >= 68 else 0
            if timestamp > 0:
                ts = datetime.datetime(1601,1,1) + datetime.timedelta(microseconds=timestamp//10)
                print(f"  {decoded_name}: Runs={run_count}, Focus={focus_count}, Last={ts}")
            else:
                print(f"  {decoded_name}: Runs={run_count}, Focus={focus_count}")
PYEOF

# Extract Recent Documents (MRU lists)
perl /opt/regripper/rip.pl -r

Step 5: Extract System and Network Information

# Computer name and OS version from SYSTEM hive
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p compname > /cases/case-2024-001/analysis/system_info.txt

# Network interfaces and configuration
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p nic2 >> /cases/case-2024-001/analysis/system_info.txt

# Wireless network history
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SOFTWARE \
   -p networklist > /cases/case-2024-001/analysis/network_history.txt

# Timezone configuration
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p timezone > /cases/case-2024-001/analysis/timezone.txt

# Shutdown time
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p shutdown > /cases/case-2024-001/analysis/shutdown.txt

# Installed software from Uninstall keys
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SOFTWARE \
   -p uninstall > /cases/case-2024-001/analysis/installed_software.txt

Key Concepts

Tools & Systems

Common Scenarios

Scenario 1: Malware Persistence Investigation Extract SOFTWARE and NTUSER.DAT hives, check all Run/RunOnce keys for unauthorized entries, examine services for suspicious additions, check scheduled tasks registry keys, correlate autorun timestamps with malware execution timeline.

Scenario 2: User Activity Reconstruction Analyze UserAssist for program execution history, examine RecentDocs for accessed files, check TypedPaths for Explorer navigation, extract ShellBags for folder access patterns, build a timeline of user activity around the incident window.

Scenario 3: Unauthorized Software Detection Parse Uninstall keys for all installed applications, compare against approved software baseline, check BAM/DAM for recently executed programs not in approved list, examine AppCompatCache for execution evidence even after uninstallation.

Scenario 4: USB Data Exfiltration Investigation Extract USBSTOR entries from SYSTEM hive for connected devices, correlate device serial numbers with MountedDevices, check NTUSER.DAT MountPoints2 for user access to removable media, examine SetupAPI logs for first-connection timestamps.

Output Format

Registry Analysis Summary:
  System: DESKTOP-ABC123 (Windows 10 Pro Build 19041)
  Timezone: Eastern Standard Time (UTC-5)
  Last Shutdown: 2024-01-18 23:45:12 UTC

  Autorun Entries:
    HKLM Run:     5 entries (1 suspicious: "updater.exe" -> C:\ProgramData\svc\updater.exe)
    HKCU Run:     3 entries (all legitimate)
    Services:     142 entries (2 unknown: "WinDefSvc", "SysMonAgent")

  User Activity (NTUSER.DAT):
    UserAssist Programs:  234 entries
    Recent Documents:     89 entries
    Typed URLs:           45 entries
    Typed Paths:          12 entries

  USB Devices Connected:
    - Kingston DataTraveler (Serial: 0019E06B4521) - First: 2024-01-10, Last: 2024-01-18
    - WD My Passport (Serial: 575834314131) - First: 2024-01-15, Last: 2024-01-15

  Installed Software:     127 applications
  Suspicious Findings:    3 items flagged for review

More skills from mukul975/Anthropic-Cybersecurity-Skills

  • Aabusing-dpapi-for-credential-accessExtract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket's dpapi.py, including domain-wide decryption via the DPAPI backup key. Use during authorized red-team credential-access engagements after gaining a foothold or when triaging DPAPI blobs pulled from a host.
  • Aabusing-shadow-credentials-for-privescTake over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target's NT hash without a password reset. Use when BloodHound shows GenericWrite/GenericAll/AddKeyCredentialLink over a target, as a stealthier alternative to ForceChangePassword, during authorized red-team engagements.
  • Aachieving-cmmc-level-2-compliancePrepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
  • Aacquiring-disk-image-with-dd-and-dcflddCreate forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.
  • Aanalyzing-active-directory-acl-abuseDetect dangerous ACL misconfigurations in Active Directory using ldap3
  • Aanalyzing-android-malware-with-apktoolPerform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.
  • Danalyzing-api-gateway-access-logs'Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect
  • Aanalyzing-apt-group-with-mitre-navigatorQuery ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator visualizations for threat-intel reporting.
  • Aanalyzing-azure-activity-logs-for-threats'Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query
  • Aanalyzing-bootkit-and-rootkit-samples'Analyzes bootkit and advanced rootkit malware infecting the Master
  • Aanalyzing-browser-forensics-with-hindsightParse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user's web activity from a browser profile.
  • Aanalyzing-campaign-attribution-evidenceSystematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

All agent skills → · MCP servers