performing-endpoint-forensics-investigation▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Performs digital forensics investigation on compromised endpoints including memory acquisition, disk imaging, artifact analysis, and timeline reconstruction. Use when investigating security incidents, collecting evidence for legal proceedings, or analyzing endpoint compromise scope. Activates for requests involving endpoint forensics, memory analysis, disk forensics, or incident investigation.
| name | performing-endpoint-forensics-investigation |
| description | 'Performs digital forensics investigation on compromised endpoints including memory acquisition, disk imaging, artifact analysis, and timeline reconstruction. Use when investigating security incidents, collecting evidence for legal proceedings, or analyzing endpoint compromise scope. Activates for requests involving endpoint forensics, memory analysis, disk forensics, or incident investigation. ' |
| domain | cybersecurity |
| subdomain | endpoint-security |
| tags | - endpoint - forensics - memory-analysis - disk-imaging - incident-investigation - Volatility |
| version | 1.0.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - PR.PS-01 - PR.PS-02 - DE.CM-01 - PR.IR-01 |
Performing Endpoint Forensics Investigation
When to Use
Use this skill when:
- Investigating a confirmed or suspected endpoint compromise requiring forensic analysis
- Collecting volatile and non-volatile evidence for incident response or legal proceedings
- Analyzing memory dumps for malware, injected code, or credential theft artifacts
- Reconstructing attacker timelines from endpoint artifacts (prefetch, shimcache, amcache)
Do not use this skill for live threat hunting (use EDR/SIEM) or network forensics.
Prerequisites
- Forensic workstation with analysis tools (Volatility 3, KAPE, Autopsy, Eric Zimmerman tools)
- Write-blocker for disk imaging (hardware or software)
- Secure evidence storage with chain-of-custody documentation
- Memory acquisition tool (WinPMEM, FTK Imager, Magnet RAM Capture)
- Administrative access to the target endpoint (or physical access)
Workflow
Step 1: Evidence Preservation (Order of Volatility)
Collect evidence from most volatile to least volatile:
1. System memory (RAM) - Most volatile
2. Network connections and routing tables
3. Running processes and open files
4. Disk contents (file system)
5. Removable media
6. Logs and backup data - Least volatile
Memory Acquisition:
# WinPMEM (Windows)
winpmem_mini_x64.exe memdump.raw
# FTK Imager - Create memory capture via GUI
# File → Capture Memory → Destination path → Capture Memory
# Linux (LiME kernel module)
sudo insmod lime.ko "path=/evidence/memory.lime format=lime"
Volatile Data Collection:
# Capture running processes
Get-Process | Export-Csv "evidence\processes.csv" -NoTypeInformation
tasklist /v > "evidence\tasklist.txt"
# Capture network connections
netstat -anob > "evidence\netstat.txt"
Get-NetTCPConnection | Export-Csv "evidence\tcp_connections.csv"
# Capture logged-on users
query user > "evidence\logged_users.txt"
# Capture scheduled tasks
schtasks /query /fo CSV /v > "evidence\scheduled_tasks.csv"
# Capture services
Get-Service | Export-Csv "evidence\services.csv"
# Capture DNS cache
ipconfig /displaydns > "evidence\dns_cache.txt"
Step 2: Disk Imaging
# FTK Imager - Create forensic disk image
# File → Create Disk Image → Physical Drive → E01 format
# Always verify image hash (MD5/SHA1) matches source
# dd (Linux)
sudo dc3dd if=/dev/sda of=/evidence/disk.dd hash=sha256 log=/evidence/imaging.log
# Verify image integrity
sha256sum /evidence/disk.dd
# Compare with hash generated during imaging
Step 3: Memory Analysis with Volatility 3
# Identify OS profile
vol -f memdump.raw windows.info
# List running processes
vol -f memdump.raw windows.pslist
vol -f memdump.raw windows.pstree
# Find hidden processes
vol -f memdump.raw windows.psscan
# Analyze network connections
vol -f memdump.raw windows.netscan
# Detect process injection
vol -f memdump.raw windows.malfind
# Extract command line arguments
vol -f memdump.raw windows.cmdline
# Analyze DLLs loaded by processes
vol -f memdump.raw windows.dlllist --pid 1234
# Extract files from memory
vol -f memdump.raw windows.filescan | grep -i "suspicious"
vol -f memdump.raw windows.dumpfiles --pid 1234
# Detect credential theft
vol -f memdump.raw windows.hashdump
vol -f memdump.raw windows.lsadump
# Registry analysis from memory
vol -f memdump.raw windows.registry.printkey --key "Software\Microsoft\Windows\CurrentVersion\Run"
Step 4: Windows Artifact Analysis
Key forensic artifacts and their tools:
Prefetch Files (C:\Windows\Prefetch\):
Tool: PECmd.exe (Eric Zimmerman)
Shows: Program execution history with timestamps and run counts
Command: PECmd.exe -d "C:\Windows\Prefetch" --csv output\
ShimCache (AppCompatCache):
Tool: AppCompatCacheParser.exe
Shows: Programs that existed on system (even if deleted)
Command: AppCompatCacheParser.exe -f SYSTEM --csv output\
AmCache (C:\Windows\appcompat\Programs\Amcache.hve):
Tool: AmcacheParser.exe
Shows: Program execution with SHA1 hashes and install timestamps
Command: AmcacheParser.exe -f Amcache.hve --csv output\
NTFS artifacts ($MFT, $UsnJrnl, $LogFile):
Tool: MFTECmd.exe
Shows: Complete file system timeline including deleted files
Command: MFTECmd.exe -f "$MFT" --csv output\
Event Logs:
Tool: EvtxECmd.exe
Shows: Security, System, PowerShell, Sysmon events
Command: EvtxECmd.exe -d "C:\Windows\System32\winevt\Logs" --csv output\
Registry Hives (SAM, SYSTEM, SOFTWARE, NTUSER.DAT):
Tool: RECmd.exe with batch files
Shows: User accounts, services, installed software, USB history
Command: RECmd.exe -d "C:\Windows\System32\config" --bn BatchExamples\RECmd_Batch_MC.reb --csv output\
Step 5: Timeline Reconstruction
# Use KAPE for automated artifact collection
kape.exe --tsource C: --tdest C:\evidence\kape_output \
--target KapeTriage --module !EZParser
# Create super timeline with plaso/log2timeline
log2timeline.py timeline.plaso disk_image.E01
psort.py -o l2tcsv timeline.plaso -w timeline.csv
# Filter timeline around incident timeframe
psort.py -o l2tcsv timeline.plaso "date > '2026-02-20' AND date < '2026-02-22'" -w filtered_timeline.csv
Step 6: Document Findings
Structure forensic report:
1. Executive Summary
2. Scope and Methodology
3. Evidence Inventory (with chain of custody)
4. Timeline of Events
5. Findings and Analysis
- Initial access vector
- Persistence mechanisms
- Lateral movement
- Data access/exfiltration
6. Indicators of Compromise (IOCs)
7. Recommendations
8. Appendices (tool output, hashes, raw evidence)
Key Concepts
| Term | Definition |
|---|---|
| Order of Volatility | Evidence collection priority from most volatile (RAM) to least volatile (backups) |
| Chain of Custody | Documented record of evidence handling from collection to presentation |
| Write Blocker | Hardware or software device that prevents modification of source evidence |
| Super Timeline | Consolidated chronological view of all artifact timestamps for incident reconstruction |
| Prefetch | Windows artifact recording program execution history |
| ShimCache | Application compatibility artifact tracking program existence on endpoint |
Tools & Systems
- Volatility 3: Memory forensics framework for analyzing RAM dumps
- KAPE (Kroll Artifact Parser and Extractor): Automated triage collection and parsing
- Eric Zimmerman Tools: Suite of Windows artifact parsers (PECmd, MFTECmd, RECmd, etc.)
- Autopsy/Sleuth Kit: Disk forensics platform for file system analysis
- FTK Imager: Forensic imaging and memory acquisition tool
- Plaso/log2timeline: Super timeline creation framework
Common Pitfalls
- Modifying evidence on live system: Always image before analysis. Running tools on a live system alters timestamps and memory state.
- Forgetting chain of custody: Evidence without documented chain of custody is inadmissible in legal proceedings.
- Analyzing only disk, ignoring memory: In-memory-only malware (fileless attacks) leaves no disk artifacts. Always capture memory first.
- Not hashing evidence: All evidence must be cryptographically hashed at collection time to prove integrity.
- Tunnel vision: Focusing on one artifact when the timeline tells a broader story. Always build a comprehensive timeline.
How to use performing-endpoint-forensics-investigation on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add performing-endpoint-forensics-investigation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-endpoint-forensics-investigation from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performing-endpoint-forensics-investigation. Access the skill through slash commands (e.g., /performing-endpoint-forensics-investigation) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★38 reviews- ★★★★★Chaitanya Patil· Dec 24, 2024
I recommend performing-endpoint-forensics-investigation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Benjamin Taylor· Dec 20, 2024
performing-endpoint-forensics-investigation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Arjun Okafor· Dec 8, 2024
Useful defaults in performing-endpoint-forensics-investigation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Arjun Perez· Dec 8, 2024
performing-endpoint-forensics-investigation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Zara Rahman· Nov 27, 2024
I recommend performing-endpoint-forensics-investigation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Arjun Gill· Nov 27, 2024
performing-endpoint-forensics-investigation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Piyush G· Nov 15, 2024
Useful defaults in performing-endpoint-forensics-investigation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Zara Abbas· Nov 15, 2024
Keeps context tight: performing-endpoint-forensics-investigation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Layla Brown· Nov 11, 2024
Solid pick for teams standardizing on skills: performing-endpoint-forensics-investigation is focused, and the summary matches what you get after install.
- ★★★★★Diego Bhatia· Oct 18, 2024
performing-endpoint-forensics-investigation reduced setup friction for our internal harness; good balance of opinion and flexibility.
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