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adriannoes/awesome-agentic-ai/cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-velociraptor-for-ir-collection/SKILL.md

implementing-velociraptor-for-ir-collection

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.

Source repository stars
52
Declared platforms
0
Static risk flags
2
Last source update
2026-08-22
Source checked
2026-08-28

Decision brief

What it does: where it fits

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.

Best for

  • 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

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/adriannoes/awesome-agentic-ai --skill "cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-velociraptor-for-ir-collection"
Safe inspection promptEditorial

Inspect the Agent Skill "implementing-velociraptor-for-ir-collection" from https://github.com/adriannoes/awesome-agentic-ai/blob/7f71af8164e8f5a775253417aa405b5d9d063faf/cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-velociraptor-for-ir-collection/SKILL.md at commit 7f71af8164e8f5a775253417aa405b5d9d063faf. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    When to Use

    When deploying or configuring implementing velociraptor for ir collection capabilities in your environment

    When deploying or configuring implementing velociraptor for ir collection capabilities in your environmentWhen establishing security controls aligned to compliance requirementsWhen building or improving security architecture for this domain
  2. 02

    Prerequisites

    Familiarity with incident response concepts and tools

    Familiarity with incident response concepts and toolsAccess to a test or lab environment for safe executionPython 3.8+ with required dependencies installed
  3. 03

    Architecture

    Velociraptor Server: Central management console with web UI and API

    Velociraptor Server: Central management console with web UI and APIVelociraptor Client (Agent): Lightweight agent deployed to endpointsFleetspeak: Communication framework between client and server
  4. 04

    Components

    Velociraptor Server: Central management console with web UI and API

    Velociraptor Server: Central management console with web UI and APIVelociraptor Client (Agent): Lightweight agent deployed to endpointsFleetspeak: Communication framework between client and server
  5. 05

    Supported Platforms

    Windows (7+, Server 2008R2+)

    Windows (7+, Server 2008R2+)Linux (Debian, Ubuntu, CentOS, RHEL)macOS (10.13+)

Permission review

Static risk signals and limitations

Network access

medium · line 42

The documentation includes network, browsing, or remote request actions.

wget https://github.com/Velocidex/velociraptor/releases/latest/download/velociraptor-linux-amd64

Runs scripts

medium · line 73

The documentation asks the agent to run terminal commands or scripts.

docker run --name velociraptor \

Network access

medium · line 242

The documentation includes network, browsing, or remote request actions.

https://elastic.local:9200

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score91/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars52SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
adriannoes/awesome-agentic-ai
Skill path
cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-velociraptor-for-ir-collection/SKILL.md
Commit
7f71af8164e8f5a775253417aa405b5d9d063faf
License
MIT
Collected
2026-08-28
Default branch
main
View the original SKILL.md

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()

Linux Forensic Artifacts

-- Collect auth logs
SELECT * FROM Artifact.Linux.Sys.AuthLogs()

-- Collect bash history
SELECT * FROM Artifact.Linux.Forensics.BashHistory()

-- Collect crontab entries
SELECT * FROM Artifact.Linux.Sys.Crontab()

-- Collect running processes
SELECT * FROM Artifact.Linux.Sys.Pslist()

-- Collect network connections
SELECT * FROM Artifact.Linux.Network.Netstat()

-- Collect SSH authorized keys
SELECT * FROM Artifact.Linux.Ssh.AuthorizedKeys()

-- Collect systemd services
SELECT * FROM Artifact.Linux.Services()

Triage Collection (All-in-One)

-- Windows Triage Collection artifact
-- Collects event logs, prefetch, registry, browser data, and more
SELECT * FROM Artifact.Windows.KapeFiles.Targets(
  Device="C:",
  _AllFiles=FALSE,
  _EventLogs=TRUE,
  _Prefetch=TRUE,
  _RegistryHives=TRUE,
  _WebBrowsers=TRUE,
  _WindowsTimeline=TRUE
)

Hunt Operations

Creating a Hunt

1. Navigate to Hunt Manager in Velociraptor Web UI
2. Click "New Hunt"
3. Configure:
   - Description: "IR Triage - Case 2025-001"
   - Include/Exclude labels for targeting
   - Artifact selection (e.g., Windows.Forensics.Prefetch)
   - Resource limits (CPU, IOPS, timeout)
4. Launch hunt
5. Monitor progress in real-time

VQL Hunt Examples

-- Hunt for specific file hash across all endpoints
SELECT * FROM Artifact.Generic.Detection.HashHunter(
  Hashes="e99a18c428cb38d5f260853678922e03"
)

-- Hunt for YARA signatures in memory
SELECT * FROM Artifact.Windows.Detection.Yara.Process(
  YaraRule='rule malware { strings: $s1 = "malicious_string" condition: $s1 }'
)

-- Hunt for Sigma rule matches in event logs
SELECT * FROM Artifact.Server.Import.SigmaRules()

-- Hunt for suspicious scheduled tasks
SELECT * FROM Artifact.Windows.System.TaskScheduler()
WHERE Command =~ "powershell|cmd|wscript|mshta|rundll32"

-- Hunt for processes with network connections to suspicious IPs
SELECT * FROM Artifact.Windows.Network.Netstat()
WHERE RemoteAddr =~ "10\\.13\\.37\\."

Real-Time Monitoring

-- Monitor for new process creation
SELECT * FROM watch_etw(guid="{22fb2cd6-0e7b-422b-a0c7-2fad1fd0e716}")
WHERE EventData.ImageName =~ "powershell|cmd|wscript"

-- Monitor file system changes
SELECT * FROM watch_directory(path="C:/Windows/Temp/")

-- Monitor registry changes
SELECT * FROM watch_registry(key="HKLM/SOFTWARE/Microsoft/Windows/CurrentVersion/Run/**")

Integration with SIEM/SOAR

Splunk Integration

Velociraptor Server --> Elastic/OpenSearch --> Splunk HEC
                   --> Direct syslog forwarding
                   --> Velociraptor API --> Custom scripts --> Splunk

Elastic Stack Integration

# Velociraptor server config for Elastic output
Monitoring:
  elastic:
    addresses:
      - https://elastic.local:9200
    username: velociraptor
    password: secure_password
    index: velociraptor

MITRE ATT&CK Mapping

TechniqueVQL Artifact
T1059 - Command ScriptingWindows.EventLogs.EvtxHunter (4104, 4688)
T1053 - Scheduled TaskWindows.System.TaskScheduler
T1547 - Boot/Logon AutostartWindows.Persistence.PermanentWMIEvents
T1003 - OS Credential DumpingWindows.Detection.Yara.Process
T1021 - Remote ServicesWindows.EventLogs.EvtxHunter (4624 Type 3/10)
T1070 - Indicator RemovalWindows.EventLogs.Cleared

References

Frequently asked questions

What to verify before installation and use

What does the implementing-velociraptor-for-ir-collection source document cover?

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.

How do I install implementing-velociraptor-for-ir-collection?

The source record exposes this install command: npx skills add https://github.com/adriannoes/awesome-agentic-ai --skill "cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/implementing-velociraptor-for-ir-collection". Inspect the command and pinned source before running it.

Which permission-related actions were detected?

Static rules flagged network, exec-script in the source; the page lists the matching lines and excerpts.

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