mantis-configure

Configures sandbox mechanisms, AI models, and credentials for Mantis security review pipelines.

865|94|Updated Jun 15, 2026
One-click install
npx skills add https://github.com/google/mantis --skill mantis-configure
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: mantis-configure
Source: https://github.com/google/mantis/tree/main/reference/skills/mantis-configure
Command: npx skills add https://github.com/google/mantis --skill mantis-configure

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Setting up an isolated environment for AI-driven security review campaigns requires manually wiring sandbox isolation, LLM credentials, and model endpoints, and misconfiguration is often only discovered after a campaign fails mid-run.

Core Features & Use Cases

  • Sandbox Configuration: Selects between static-only, gVisor container, microsandbox microVM, and GCE ephemeral VM isolation levels based on host capabilities.
  • Model and Endpoint Management: Switches between Gemini, Claude on Vertex AI, MaaS models, and custom OpenAI-compatible endpoints with reasoning effort and timeout controls.
  • Fast Preflight Validation: Runs 1-2 second checks verifying LLM reachability and sandbox readiness before launching campaigns.
  • Use Case: Before running an unattended vulnerability discovery campaign on a new host, auto-detect available isolation mechanisms (KVM, runsc, gcloud), configure the strongest available sandbox, and verify credentials in seconds.

Quick Start

Ask the agent to auto-detect host capabilities and configure the Mantis pipeline with the best available sandbox, then run a preflight test to confirm everything is operational.

Frequently Asked Questions about mantis-configure

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I configure a sandbox for AI security review pipelines?

Run the configure script with --auto to detect host capabilities like /dev/kvm, docker/runsc, or gcloud, and it selects the strongest available sandbox. Alternatively, specify one explicitly with --sandbox static-only, gvisor, microsandbox, or gce.

How do I switch LLM models in a workflow.json configuration?

Use the --model flag with a provider-prefixed identifier such as gemini-3.7-flash, vertex_ai/claude-opus-5, or openai/{MODEL_ID}. For custom endpoints, add --api-base pointing to your OpenAI-compatible server like a local vLLM instance.

What sandbox isolation options exist for running untrusted AI-generated code?

Four tiers are supported: static-only (no execution), gVisor containers via runsc, microsandbox microVMs requiring /dev/kvm, and ephemeral GCE VMs accessed over IAP SSH tunnels. Higher tiers provide stronger kernel or hypervisor isolation.

Can I use a local vLLM or Ollama server with this configuration?

Yes, custom OpenAI-compatible endpoints are supported via the openai/{MODEL_ID} model prefix combined with --api-base pointing to your server URL. You can also set --reasoning-effort and --timeout for these endpoints.

How do I verify LLM credentials and sandbox readiness before a campaign?

Run the configure script with --test or --preflight to execute fast 1-2 second validation checks. It verifies LLM reachability and sandbox requirements, and --json outputs diagnostics in machine-readable form.

When should I use static-only mode instead of a sandboxed environment?

Use static-only when the host lacks docker, KVM, or cloud credentials, or when zero host risk is required. It disables reproducer execution and dynamic patching, limiting analysis to static source review.