implementing-velociraptor-for-ir-collection
Helps your agent configure Velociraptor for collecting endpoint data during incident response.
What it does
Velociraptor is a tool for gathering detailed information from computers. It helps security teams investigate incidents, like attacks, by collecting data quickly and efficiently from many computers at once.
When to use it
- ✓When setting up Velociraptor to collect security data.
- ✓When establishing security controls to meet compliance.
- ✓When building or improving a system's security architecture.
Example of what to ask your AI
“Set up Velociraptor to collect Windows event logs.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
Velociraptor has a central server and agents (small programs) installed on computers. The server sends commands to the agents to collect specific information using a query language (VQL). The collected data is sent back to the server for analysis.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-velociraptor-for-ir-collectionOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/implementing-velociraptor-for-ir-collection/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Implementing Velociraptor for IR Collection
Overview
Velociraptor is an advanced open-source endpoint monitoring, digital forensics, and incident response platform developed by Rapid7. It uses the Velociraptor Query Language (VQL) to create custom artifacts that collect, query, and monitor almost any aspect of an endpoint. Velociraptor enables incident response teams to rapidly collect and examine forensic artifacts from across a network, supporting large-scale deployments with minimal performance impact. The client-server architecture with Fleetspeak communication enables real-time data collection from thousands of endpoints simultaneously, with offline endpoints picking up hunts when they reconnect.
When to Use
- When deploying or configuring implementing velociraptor for ir collection capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Familiarity with incident response concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Architecture
Components
- Velociraptor Server: Central management console with web UI and API
- Velociraptor Client (Agent): Lightweight agent deployed to endpoints
- Fleetspeak: Communication framework between client and server
- VQL Engine: Query language engine for artifact collection
- Filestore: Server-side storage for collected artifacts
- Datastore: Metadata storage for hunts, flows, and client information
Supported Platforms
- Windows (7+, Server 2008R2+)
- Linux (Debian, Ubuntu, CentOS, RHEL)
- macOS (10.13+)
Deployment
Server Installation
# Download latest release
wget https://github.com/Velocidex/velociraptor/releases/latest/download/velociraptor-linux-amd64
# Generate server configuration
./velociraptor-linux-amd64 config generate -i
# Start the server
./velociraptor-linux-amd64 --config server.config.yaml frontend
# Or run as systemd service
sudo cp velociraptor-linux-amd64 /usr/local/bin/velociraptor
sudo velociraptor --config /etc/velociraptor/server.config.yaml service install
Client Deployment
# Repack client MSI for Windows deployment
velociraptor --config server.config.yaml config client > client.config.yaml
velociraptor config repack --msi velociraptor-windows-amd64.msi client.config.yaml output.msi
# Deploy via Group Policy, SCCM, or Intune
# Client runs as a Windows service: "Velociraptor"
# Linux client deployment
velociraptor --config client.config.yaml client -v
# macOS client deployment
velociraptor --config client.config.yaml client -v
Docker Deployment
docker run --name velociraptor \
-v /opt/velociraptor:/velociraptor/data \
-p 8000:8000 -p 8001:8001 -p 8889:8889 \
velocidex/velociraptor
Core IR Artifact Collection
Windows Forensic Artifacts
-- Collect Windows Event Logs
SELECT * FROM Artifact.Windows.EventLogs.EvtxHunter(
EvtxGlob="C:/Windows/System32/winevt/Logs/*.evtx",
IDRegex="4624|4625|4648|4672|4688|4698|4769|7045"
)
-- Collect Prefetch files for execution evidence
SELECT * FROM Artifact.Windows.Forensics.Prefetch()
-- Collect Shimcache entries
SELECT * FROM Artifact.Windows.Registry.AppCompatCache()
-- Collect Amcache entries
SELECT * FROM Artifact.Windows.Forensics.Amcache()
-- Collect UserAssist data
SELECT * FROM Artifact.Windows.Forensics.UserAssist()
-- Collect NTFS MFT timestamps
SELECT * FROM Artifact.Windows.NTFS.MFT(
MFTFilename="C:/$MFT",
FileRegex=".(exe|dll|ps1|bat|cmd)$"
)
-- Collect scheduled tasks
SELECT * FROM Artifact.Windows.System.TaskScheduler()
-- Collect running processes with hashes
SELECT * FROM Artifact.Windows.System.Pslist()
-- Collect network connections
SELECT * FROM Artifact.Windows.Network.Netstat()
-- Collect DNS cache
SELECT * FROM Artifact.Windows.Network.DNSCache()
-- Collect browser history
SELECT * FROM Artifact.Windows.Applications.Chrome.History()
-- Collect PowerShell history
SELECT * FROM Artifact.Windows.Forensics.PowerShellHistory()
-- Collect autoruns/persistence
SELECT * FROM Artifact.Windows.Persistence.PermanentWMIEvents()
SELECT * FROM Artifact.Windows.System.Services()
SELECT * FROM Artifact.Windows.System.StartupItems()
…
FAQ
What is VQL?
VQL (Velociraptor Query Language) is the language used to tell Velociraptor what information to collect from computers.
What are 'hunts'?
'Hunts' are active searches you can set up to find specific information or signs of compromise across multiple computers.
Does Velociraptor affect computer performance?
Velociraptor is designed to have minimal impact on computer performance, even when collecting data from thousands of endpoints simultaneously.
Details
Original description by the author
Deploy and configure Velociraptor for scalable endpoint forensic artifact collection during incident response using VQL queries, hunts, and pre-built artifact packs across Windows, Linux, and macOS environments.
Popularity
- GitHub stars
- 35k
- Ranking position
- #5972
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
More skills from this repository
abusing-dpapi-for-credential-access
Gives your AI agent ready-made instructions for a specific task, so it does it better and faster.
mukul975/Anthropic-Cybersecurity-Skills
abusing-shadow-credentials-for-privesc
Teaches your AI agent to write clear, well-structured text.
mukul975/Anthropic-Cybersecurity-Skills
achieving-cmmc-level-2-compliance
Helps your agent prepare an environment for CMMC Level 2 compliance. It implements security requirements and manages necessary documentation.
mukul975/Anthropic-Cybersecurity-Skills
acquiring-disk-image-with-dd-and-dcfldd
Helps your agent create exact copies of hard drives. This is useful for ensuring no information is lost or altered during investigations.
mukul975/Anthropic-Cybersecurity-Skills
Similar skills
dashboard-builder
Helps your agent create useful monitoring dashboards. It turns data into charts that answer important system questions.
affaan-m/ECC
dev-team
Simulates a collaborative development team where different roles respond to a problem together. It's useful for getting multiple perspectives without manually switching agents.
affaan-m/ECC
ecc-council
Has your agent convene a four-voice council for difficult decisions. It's useful when there are multiple good options and debate is needed before choosing.
affaan-m/ECC