gemini-cli

Orchestrate Gemini runtime agents, tools, and extensions from the command line.

1|Updated Jan 22, 2026
One-click install
npx skills add https://github.com/guardian-intelligence/apm2 --skill gemini-cli-guardian-intelligence
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gemini-cli
Source: https://github.com/guardian-intelligence/apm2/tree/main/documents/skills/gemini-cli
Command: npx skills add https://github.com/guardian-intelligence/apm2 --skill gemini-cli-guardian-intelligence

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Gemini CLI provides a structured, scriptable interface to interact with the Gemini runtime, enabling developers and operators to orchestrate AI agents, tools, and extensions from the command line.

Core Features & Use Cases

  • Interactive mode for exploratory work and debugging in a TTY.
  • Headless mode for automation and CI pipelines with deterministic outputs.
  • Extension and tool mediation to compose complex AI workflows safely.
  • Sandbox-aware execution and workspace management for reproducible runs.
  • Use Case: Integrate Gemini in a data science project to run a sequence of agent tasks and capture tool results in a replayable log.

Quick Start

Install Node.js (≥20) and install dependencies, then run the production entry bin.gemini to start the CLI in your project. For a quick look, run bin.gemini --help to see available commands.

Frequently Asked Questions about gemini-cli

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

FAQPage Schema
How do I automate Gemini runtime orchestration from the command line?

Automate Gemini runtime orchestration from the command line by running the CLI in headless mode, which provides deterministic outputs for CI pipelines. You configure execution via a CLI config with policy and extension support to manage agent tasks.

Does the Gemini CLI require a specific Node.js version and workspace layout?

The Gemini CLI requires Node.js ≥20 and a workspace monorepo layout. It uses esbuild bundling for production, ensuring sandbox-aware execution and reproducible runs within your configured project structure.

What is the difference between interactive and headless mode for command-line AI workflows?

Interactive mode supports exploratory work and debugging in a TTY, while headless mode enables automation and CI pipelines with deterministic outputs. Both modes handle workspace management, extension handling, and tool mediation during AI executions.

How do I compose complex AI workflows safely using command-line tool mediation?

Compose complex AI workflows safely by using the CLI's extension and tool mediation features. Configuration via a CLI config with policy and extension support allows you to sequence agent tasks and capture tool results in a replayable log.

Can I capture and replay tool results from agent tasks in a data science project?

You can capture tool results in a replayable log by running a sequence of agent tasks through the CLI. Sandbox-aware execution and workspace management ensure that these data science project runs remain reproducible.

When should I use sandbox-aware execution and workspace management for command-line AI agents?

Use sandbox-aware execution and workspace management when you need reproducible runs from your command-line AI agents. This approach isolates execution environments, ensuring deterministic outputs for automation and safe tool mediation.