Agent Skills with Antigravity CLI

Github Repo: https://github.com/10xroadmap/agent-s... The video introduces **Antigravity CLI (`agy`)**, a native terminal interface designed to inject Large Language Model (LLM) and agentic capabilities directly into a developer's shell environment. It functions as an autonomous DevOps and Systems engineer capable of handling complex software development lifecycles (SDLC) and executing modular "Agent Skills." --- Core Features & Use Cases 1. Where to Use Antigravity CLI Because the CLI inherits the user's native shell environment, it can operate autonomously across several domains: *SDLC Management:* Managing end-to-end software pipelines directly. *Intelligent Editing:* Executing complex, multi-file code edits with full context awareness. *Git Workflows:* Streamlining branch management, committing, and PR handling. *Infra Ops:* Autonomously deploying tasks and troubleshooting infrastructure issues. *Agentic Tasks:* Scheduling background, non-interactive worker task flows. *MCP Integration:* Connecting to databases, Docker, and APIs using the Model Context Protocol. 2. Headless Automation The CLI is capable of non-interactive automation using the `-p` flag (e.g., `agy -p "Refactor the authentication logic"`). This makes it ideal for: CI/CD pipelines Automated scripting Background processing --- Context Management (`GEMINI.md`) To prevent the need to repeat rules, boundaries, and preferences in every prompt, `agy` utilizes a project-specific, persistent context layer called `GEMINI.md`. *Identity & Role:* Establishes a persona (e.g., Senior Full-Stack Engineer expected to deliver production-ready code). *System Directives:* Enforces direct answers, prioritizing speed, efficiency, and security while removing conversational filler. *Conventions & Formatting:* Standardizes formatting rules (e.g., strict 2-space indentation, camelCase for JS/TS, JSDoc strings, and mandatory QA unit tests). *Restructured Scope:* Automatically ignores irrelevant directories like `.git/` and `node_modules/`. --- Understanding Agent Skills System Prompt vs. Agent Skills The video differentiates the core architecture of the AI into two parts: *System Prompt (The Brain & Personality):* Defines fundamental identity, tone, boundaries, and guardrails. *Agent Skills (The Hands & Toolbelt):* Modular, executable tools and instructions designed for specific, repeatable workflows. Why Use Agent Skills? Instead of bloating the AI's core memory with thousands of lines of instructions, skills use **progressive disclosure**: 1. *Discovery:* Only the skill’s title and description sit in active memory. 2. *Loading:* The agent dynamically pulls the instructions/scripts only when a relevant prompt triggers it. 3. *Execution:* The agent follows the precise steps or runs the packaged scripts to complete the task. Skill Directory Structure Skills are organized into version-controlled, portable folders: ```text my-skill/ ├── SKILL.md # Required: metadata (name, description) + instructions ├── scripts/ # Optional: executable shell commands/code ├── references/ # Optional: documentation └── assets/ # Optional: templates and resources ``` Leveraging pre-written scripts within a skill ensures **deterministic outcomes* and *token efficiency* because the LLM doesn't have to dynamically generate and debug code inside its limited context window.* --- Installation & Setup 1. *Install Package:* *Mac / Linux:* `curl -fsSL https://antigravity.google/cli/instal... | bash` *Windows (PowerShell):* `irm https://antigravity.google/cli/instal... | iex` 2. *Launch:* Run `agy` in your terminal. 3. *Authentication:* Sign in using a standard Google account to authorize the workspace capabilities. Ensure the utility is added to your system `PATH`.