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Mantis Configure

OrganizationPopular
google
mantis-configure

Configures and validates Mantis pipeline environments, sandbox mechanisms, and AI models. Use to set up workflow.json, auto-detect host capabilities, switch between sandboxes (static-only, gvisor, microsandbox, gce), select AI models, and run fast 1-2s preflight tests. Don't use for scanning source code or running attack campaigns.

Overview

Publishergoogle
Repositorymantis
Skill namemantis-configure
Stars
1.6K
Forks
154
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by google on GitHub. Read the source before you install it.

Installation

Install the Mantis Configure AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google/mantis.git /tmp/mantis
mkdir -p .claude/skills
cp -r /tmp/mantis/reference/skills/mantis-configure .claude/skills/mantis-configure
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mantis Configure in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mantis Configure on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mantis Configure is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pipeline Configurator (/mantis-configure)

System Goal

Environment and Model Configurator. Configures workflow.json with appropriate sandbox execution mechanisms, AI model providers, API endpoints, and credential bindings. Provides instantaneous preflight verification to guarantee that LLM credentials and sandbox isolation requirements are fully operational before launching security review campaigns.

Command Definition

  • Command: /mantis-configure
  • Description: Configures Mantis pipeline settings (sandboxes, models, credentials, preflight validation) in workflow.json.
  • Execution Command:
    bash
    python3 "${MANTIS_HOME:-/path/to/mantis}/reference/scripts/configure.py" [flags...]

Path Anchoring Requirement (CRITICAL): The configuration script resides within the Mantis installation directory at reference/scripts/configure.py. You MUST invoke this script via an absolute path or via $MANTIS_HOME. NEVER execute python3 reference/scripts/configure.py using a relative path inside audited target repositories.

  • CLI Options:
    • --sandbox / -s: Sandbox mechanism (static-only, gvisor, microsandbox, gce).
    • --model / -m: Default LLM model (e.g. gemini-3.7-flash, vertex_ai/claude-opus-5, vertex_ai/zai_org/glm-5.2-maas, openai/{MODEL_ID}).
    • --api-base: Custom endpoint URL for OpenAI-compatible LLM servers (e.g. http://localhost:8000/v1).
    • --reasoning-effort: Reasoning effort level (low, medium, high).
    • --timeout: LLM request timeout in seconds.
    • --project / -p: GCP Project ID (for GCE sandbox or Vertex AI routing).
    • --zone / -z: GCP Zone (e.g. us-central1-b).
    • --image / -i: Sandbox image name (e.g. mantis-sandbox-image or mantis-sandbox:latest).
    • --subnet: GCE Subnet name (e.g. mantis-isolated-subnet).
    • --workdir: Sandbox guest workdir (default: /workspace).
    • --workflow / -w: Path to workflow.json (defaults to auto-discovery).
    • --db / -d: Path to SQLite knowledge database (default: knowledge.db).
    • --auto: Auto-detects host capabilities and configures optimal settings automatically.
    • --save: Explicitly saves configuration changes to workflow.local.json.
    • --save-tracked / --global: Saves configuration changes directly to base workflow.json.
    • --interactive: Interactive step-by-step terminal wizard.
    • --test / --preflight: Executes fast (1-2s) static validation tests verifying LLM configuration format and sandbox readiness.
    • --probe / --probe-llm: Actively probes LLM reachability, provider credentials, and client dependencies with a minimal test prompt (test, max 256 tokens).
    • --show: Displays current configuration and diagnostic status.
    • --dry-run: Simulates configuration changes without modifying files.
    • --update-nodes: Updates all agent nodes in workflow.json to use the specified default model.
    • --json: Outputs configuration status and preflight diagnostics in JSON.

Supported Sandbox Mechanisms

SandboxDescriptionIsolation LevelRequirements
static-onlyStatic analysis only; reproducer and dynamic patching disabled.Zero Host RiskNone (Always available)
gvisorNetworkless OCI container executed under Google gVisor (runsc).Process & Kernel sandboxdocker or podman with runsc registered
microsandboxEphemeral Linux microVM with hardware virtualization.Virtual Machine/dev/kvm read/write access
gceHardened ephemeral Google Compute Engine VM via IAP SSH tunnel.Cloud Hypervisorgcloud CLI, active GCP auth & project

Supported Model Providers

  1. Gemini Models (Google / Vertex AI):
    • gemini-3.7-flash, gemini-3.5-flash-lite
    • vertex_ai/gemini-3.7-flash, vertex_ai/gemini-3.5-flash-lite
  2. Claude Models (Vertex AI Model Garden):
    • vertex_ai/claude-opus-5
  3. MaaS & Open Source Models (Vertex Model Garden):
    • vertex_ai/zai_org/glm-5.2-maas
  4. Custom OpenAI-Compatible Endpoints:
    • openai/{MODEL_ID} or vertex_ai/openai/{MODEL_ID}
    • Supports custom --api-base (e.g. vLLM, Ollama, LiteLLM proxy), --reasoning-effort, and --timeout.

Common CLI Workflows

1. Fast Preflight Verification (~1s) & Active Reachability Probe

Check if LLM configuration and sandbox requirements are operational:

bash
# Fast static validation (~1s):
python3 "$MANTIS_HOME/reference/scripts/configure.py" --test

# Active live reachability probe against LLM provider:
python3 "$MANTIS_HOME/reference/scripts/configure.py" --test --probe

2. Auto-Detect and Configure

Automatically inspect host capabilities (/dev/kvm, docker/runsc, gcloud) and select the best available sandbox:

bash
python3 "$MANTIS_HOME/reference/scripts/configure.py" --auto

3. Switch to Static Analysis (Zero Dependencies)

bash
python3 "$MANTIS_HOME/reference/scripts/configure.py" --sandbox static-only

4. Configure gVisor Container Sandbox

bash
python3 "$MANTIS_HOME/reference/scripts/configure.py" --sandbox gvisor --image mantis-sandbox:latest

5. Configure GCE Ephemeral Cloud Sandbox

bash
python3 "$MANTIS_HOME/reference/scripts/configure.py" --sandbox gce --project my-gcp-project --zone us-central1-b

6. Switch AI Model to Claude or Custom Endpoint

bash
# Vertex AI Claude
python3 "$MANTIS_HOME/reference/scripts/configure.py" --model vertex_ai/claude-opus-5

# Custom Local vLLM / OpenAI server
python3 "$MANTIS_HOME/reference/scripts/configure.py" --model openai/my-model --api-base http://localhost:8000/v1

7. Interactive Configuration Wizard

bash
python3 "$MANTIS_HOME/reference/scripts/configure.py" --interactive

Python API Reference

When invoked programmatically from Python:

python
from scripts.configure import (
    detect_capabilities,
    ensure_configured,
    ensure_configured_async,
    is_default_or_unconfigured,
    run_preflight_checks,
    run_preflight_checks_async,
    update_workflow_config,
)

# 1. Check if configuration contains default placeholders
is_unconf, issues = is_default_or_unconfigured(config)

# 2. Run fast preflight checks (sync or async)
ok, messages = run_preflight_checks(config, test_llm=True, test_sandbox=True)
# or: ok, messages = await run_preflight_checks_async(config)

# 3. Ensure configured (auto-resolves defaults if unconfigured)
valid_config = ensure_configured(auto=True)
# or: valid_config = await ensure_configured_async(auto=True)

Input/Output Contract

  • Reads:
    • workflow.json and optional workflow.local.json overlay
    • Host environment (virtualization devices, container engines, cloud CLI credentials)
  • Writes:
    • workflow.local.json (or workflow.json when --save-tracked is set)

Frequently asked questions

What does the Mantis Configure AI skill do?

Configures and validates Mantis pipeline environments, sandbox mechanisms, and AI models. Use to set up workflow.json, auto-detect host capabilities, switch between sandboxes (static-only, gvisor, microsandbox, gce), select AI models, and run fast 1-2s preflight tests. Don't use for scanning source code or running attack campaigns.

Why use Mantis Configure on TypingMind?

Because you install it once and use it with any model. Mantis Configure is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mantis Configure in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/mantis/tree/main/reference/skills/mantis-configure. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mantis Configure?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mantis Configure?

As many as you like. As long as a model supports skills, you can use Mantis Configure with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mantis Configure AI skill free?

Yes. It is published on GitHub by google under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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