Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/26zl/cybersec-toolkitnpx agentmods add skills/26zl/cybersec-toolkit/analyzing-windows-registry-for-artifactsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-windows-registry-for-artifacts)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-windows-registry-for-artifacts"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-windows-registry-for-artifacts/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-windows-registry-for-artifacts"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-windows-registry-for-artifacts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.03055 |
| Opus 5 | $0.00018 | $0.01528 |
| Sonnet 5 | $0.00007 | $0.00611 |
| Haiku 4.5 | $0.00004 | $0.00305 |
Grade A, and why
analyzing-windows-registry-for-artifacts scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- analyzing-windows-registry-for-artifacts — 92% identical, 24 lines differ
- analyzing-windows-registry-for-artifacts — 92% identical, 24 lines differ
- analyzing-windows-registry-for-artifacts — 92% identical, 24 lines differ
- analyzing-windows-registry-for-artifacts — 91% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section 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
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 300 lines · 37 tokens per session scan A 946728aea08d
analyzing-windows-registry-for-artifacts is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 3,055 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
analyzing-windows-registry-for-artifacts
Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.
analyzing-windows-registry-for-artifacts
Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.
analyzing-windows-registry-for-artifacts
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…
analyzing-windows-registry-for-artifacts
Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.
analyzing-windows-registry-for-artifacts
Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.
analyzing-windows-registry-for-artifacts
Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.