external/anthropic-cybersecurity-skills/skills/fleet-hunting-with-velociraptor/SKILL.md
Deploy a Velociraptor server and agents, then author VQL (Velociraptor Query Language) artifacts and run them as fleet-wide hunts, on-demand forensic collections, or standalone offline collectors. Use when hunting a TTP across hundreds or thousands of endpoints, collecting forensic artifacts during incident response without re-imaging, or generating collectors for unmanaged/air-gapped hosts.
npx skillsauth add seikaikyo/dash-skills fleet-hunting-with-velociraptorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Authorized Use Only: Velociraptor agents provide deep endpoint visibility and remote collection. Deploy only on assets you own or are authorized to monitor, in accordance with your monitoring policy and applicable law.
Velociraptor is an open-source endpoint visibility and digital-forensics platform from Rapid7/Velocidex. A single Go binary acts as server, client (agent), and CLI depending on how it is invoked and configured. Its power comes from VQL (Velociraptor Query Language) — an SQL-like language whose plugins query the live state of an endpoint (processes, files, registry, event logs, WMI, network connections, prefetch, etc.). VQL queries are packaged into reusable Artifacts, and Artifacts are run at scale as Hunts that fan out across every connected client and stream results back to the server as structured rows.
This makes Velociraptor ideal for fleet-wide threat hunting: a hypothesis ("are any hosts running suspicious PowerShell?") becomes a VQL artifact, deployed as a hunt, with results aggregated centrally in minutes. It also supports offline collectors (standalone executables that collect and bundle artifacts on air-gapped or unmanaged hosts) and live forensic notebooks.
chmod +x velociraptor-v0.*-linux-amd64
sudo mv velociraptor-v0.*-linux-amd64 /usr/local/bin/velociraptor
| ID | Official Technique Name | Relevance to this skill | |----|------------------------|--------------------------| | T1059 | Command and Scripting Interpreter | A primary hunt target — VQL artifacts surface anomalous interpreter execution (PowerShell, cmd, wscript) across the fleet for detection and triage. |
Velociraptor is a defensive hunting platform; the mapping reflects the adversary behavior the hunts are designed to detect.
The interactive generator writes a server config (TLS, datastore paths, GUI users, frontend URL). Use config generate for a self-signed lab build or the interactive -i wizard for production.
# Non-interactive: dump a default server config
velociraptor config generate > server.config.yaml
# Interactive wizard (recommended for production deployments)
velociraptor config generate -i
Create at least one administrator to log into the console.
velociraptor --config server.config.yaml user add admin --role administrator
The frontend accepts client connections; the GUI is served per the config (default https://127.0.0.1:8889).
velociraptor --config server.config.yaml frontend -v
For a quick all-in-one local lab (server + frontend + a local client in one process):
velociraptor gui
Derive the client config from the server config and run it as the client on each endpoint.
# Produce the client config (embeds server URL + CA)
velociraptor --config server.config.yaml config client > client.config.yaml
# On a Linux endpoint, run as a client (or install as a service)
velociraptor --config client.config.yaml client -v
On Windows, build an MSI/service installer from the GUI ("Server Artifacts" > deployment) or run:
velociraptor.exe --config client.config.yaml service install
Validate a query locally with query (-q) before deploying it fleet-wide. VQL is SQL-like: SELECT ... FROM plugin(...) WHERE ....
# List running processes with their command lines
velociraptor query "SELECT Pid, Name, CommandLine FROM pslist()"
# Hunt for suspicious PowerShell command lines
velociraptor query "
SELECT Pid, Name, CommandLine
FROM pslist()
WHERE Name =~ 'powershell'
AND CommandLine =~ '(?i)(-enc|frombase64string|downloadstring|-w hidden|iex)'
"
Artifacts wrap VQL into reusable, parameterized collections.
# Show available artifacts
velociraptor artifacts list
# Collect a built-in artifact and write results to a directory
velociraptor artifacts collect Windows.System.Pslist --output results.zip
In the GUI: Hunt Manager > New Hunt > select the artifact (e.g. Windows.Detection.Powershell or a custom one) and parameters > Launch. The hunt fans out to every matching client; results stream into the hunt's results table and can be exported as CSV/JSON. Equivalent server-side VQL:
-- Create a hunt programmatically via a server VQL notebook
SELECT hunt(
description="Suspicious PowerShell fleet sweep",
artifacts="Windows.Detection.Powershell"
) FROM scope()
Custom artifacts are YAML documents containing parameters and VQL sources. Save in the GUI's Artifact editor or import via artifacts:
name: Custom.Hunt.SuspiciousPowershell
description: Find encoded / download-cradle PowerShell across the fleet.
parameters:
- name: regex
default: "(?i)(-enc|frombase64string|downloadstring|-w hidden|iex)"
sources:
- query: |
SELECT Pid, Name, CommandLine, timestamp(epoch=now()) AS Collected
FROM pslist()
WHERE Name =~ "powershell" AND CommandLine =~ regex
For unmanaged/air-gapped hosts, build a standalone collector from the GUI ("Server Artifacts" > Server.Utils.CreateCollector) or via VQL; it produces a single executable that collects chosen artifacts into a ZIP for later import.
| Resource | Purpose | Link | |----------|---------|------| | Velociraptor releases | Official binaries | https://github.com/Velocidex/velociraptor/releases | | Documentation | Deployment, VQL, artifacts | https://docs.velociraptor.app/ | | VQL reference | Plugin/function reference | https://docs.velociraptor.app/vql_reference/ | | Artifact Exchange | Community artifacts | https://docs.velociraptor.app/exchange/ | | Source | GitHub repository | https://github.com/Velocidex/velociraptor |
| Command | Purpose |
|---------|---------|
| config generate [-i] | Create server config (interactive optional) |
| config client | Derive client config from server config |
| user add <name> --role administrator | Add a GUI admin |
| frontend -v | Start server frontend (client comms + GUI) |
| gui | All-in-one local lab instance |
| client -v | Run as an endpoint agent |
| service install | Install the agent as a service |
| query "<VQL>" | Run VQL ad hoc |
| artifacts list | List available artifacts |
| artifacts collect <name> --output <zip> | Collect an artifact locally |
querytools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.