Agent skill

Analyzing Windows Registry For Artifacts

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Extract and analyze Windows Registry hives with tools like RegRipper and Registry Explorer to uncover user activity, installed software, autostart/persistence entries, and evidence of system…

Apache-2.0Auto-check passedSecurity

Install Analyzing Windows Registry For Artifacts

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-windows-registry-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-registry-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-registry-for-artifacts .claude/skills/analyzing-windows-registry-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-registry-for-artifacts
GitHub stars
34k
Token cost
~2.9k tokens
SKILL.md length
427 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Extract and analyze Windows Registry hives with tools like RegRipper and Registry Explorer to uncover user activity, installed software, autostart/persistence entries, and evidence of system…

  • Works in 5 steps: Extract Registry Hives from the Forensic… → Analyze with RegRipper for Automated… → Extract Persistence and Autorun Entries → …
  • Investigating registry-based persistence
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3, git and pip; reaches github.com

What it does

Analyzing Windows Registry For Artifacts is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Extract and analyze Windows Registry hives with tools like RegRipper and Registry Explorer to uncover user activity, installed software, autostart/persistence entries, and evidence of system compromise. Use when investigating registry-based persistence, reconstructing user or system activity, or performing DFIR triage on a Windows image.

Its SKILL.md is about 2.9k 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 registry-based persistence
  • Reconstructing user
  • System activity
  • Performing DFIR triage on a Windows image

Example prompts

  • “/analyzing-windows-registry-for-artifacts”

Requirements

  • Python 3

Workflow steps

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

  1. Extract Registry Hives from the Forensic Image
  2. Analyze with RegRipper for Automated Artifact Extraction
  3. Extract Persistence and Autorun Entries
  4. Analyze User Activity Artifacts
  5. Extract System and Network Information

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
    • git
    • pip

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    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 Registry For Artifacts loads about 2.9k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 427 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

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). 427 words, ~2,942 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-windows-registry-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-registry-for-artifacts
description
Extract and analyze Windows Registry hives with tools like RegRipper and Registry Explorer to uncover user activity, installed software, autostart/persistence entries, and evidence of system compromise. Use when investigating registry-based persistence, reconstructing user or system activity, or performing DFIR triage on a Windows image.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, windows-registry, artifact-analysis, regripper, registry-explorer, evidence-collection
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1012, T1547.001, T1112, T1003.002, T1025

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
bash
# 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
bash
# 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
bash
# 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.DAT")

user_autorun = [
    "Software\\Microsoft\\Windows\\CurrentVersion\\Run",
    "Software\\Microsoft\\Windows\\CurrentVersion\\RunOnce",
    "Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\StartupApproved\\Run"
]

for path in user_autorun:
    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
PYEOF
Step 4: Analyze User Activity Artifacts
bash
# 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 /cases/case-2024-001/registry/NTUSER.DAT \
   -p recentdocs > /cases/case-2024-001/analysis/recentdocs.txt

# Extract typed URLs (browser)
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/NTUSER.DAT \
   -p typedurls > /cases/case-2024-001/analysis/typedurls.txt

# Extract typed paths in Explorer
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/NTUSER.DAT \
   -p typedpaths > /cases/case-2024-001/analysis/typedpaths.txt
Step 5: Extract System and Network Information
bash
# 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

ConceptDescription
Registry hiveBinary file storing a section of the registry (SAM, SYSTEM, SOFTWARE, NTUSER.DAT)
MRU (Most Recently Used)Lists tracking recently accessed files, commands, and search terms
UserAssistROT13-encoded registry entries tracking program execution with timestamps
ShimCacheApplication compatibility cache recording executed programs
AmCacheDetailed execution history including SHA-1 hashes of executables
BAM/DAMBackground/Desktop Activity Moderator tracking program execution in Win10+
Last Write TimeTimestamp on registry keys indicating when they were last modified
Transaction logsJournal files allowing recovery of registry state after improper shutdown

Tools & Systems

ToolPurpose
RegRipperAutomated registry artifact extraction with plugin architecture
Registry ExplorerEric Zimmerman GUI tool for interactive registry analysis
python-registryPython library for programmatic registry hive parsing
RECmdEric Zimmerman command-line registry analysis tool
yarpYet Another Registry Parser for Python-based analysis
AppCompatCacheParserDedicated ShimCache/AppCompatCache parser
AmcacheParserDedicated AmCache.hve analysis tool
ShellBags ExplorerSpecialized tool for analyzing ShellBag artifacts
Show full SKILL.md (146 more words)Show less

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

© 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-registry-for-artifacts of mukul975/Anthropic-Cybersecurity-Skills.

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

Open the folder on GitHubat commit 54a7988

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Categories

Questions about Analyzing Windows Registry For Artifacts

What does Analyzing Windows Registry For Artifacts do?

Extract and analyze Windows Registry hives with tools like RegRipper and Registry Explorer to uncover user activity, installed software, autostart/persistence entries, and evidence of system…. Analyzing Windows Registry For Artifacts is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Extract and analyze Windows Registry hives with tools like RegRipper and Registry Explorer to uncover user activity, installed software, autostart/persistence entries, and evidence of system compromise.

When should I use Analyzing Windows Registry For Artifacts?

Analyzing Windows Registry For Artifacts fits situations like: investigating registry-based persistence; reconstructing user; system activity; performing DFIR triage on a Windows image.

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

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

How do I install Analyzing Windows Registry For Artifacts in Codex?

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

Can I use Analyzing Windows Registry 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-registry-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-registry-for-artifacts, .gemini/skills/analyzing-windows-registry-for-artifacts, .github/skills/analyzing-windows-registry-for-artifacts and .opencode/skills/analyzing-windows-registry-for-artifacts in your project.

What does Analyzing Windows Registry For Artifacts need to run?

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

Does Analyzing Windows Registry For Artifacts access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Analyzing Windows Registry 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 Registry For Artifacts use?

Analyzing Windows Registry 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 Registry For Artifacts use?

About 2.9k tokens (SKILL.md is roughly 12k 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 534 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Windows Registry For Artifacts?

Skills that share tags, products or a category with Analyzing Windows Registry 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 Registry 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.