# Antigravity Features and Practical Usage 🚀 A "USB-C port for AI" that safely connects your agent to your databases and internal tools—that is Antigravity's MCP. 📌 Title and Feature URL Title: MCP URL: 📝 Overview Antigravity supports the Model Context Protocol (MCP), a standard that lets the editor securely connect to your local tools, databases, and external services. MCP acts as a "USB-C port for AI," standardizing how AI agents and large language models plug into different data sources. 🔧 How It Works MCP works as a standardized plug-in mechanism: - MCP is an open standard that lets AI agents securely connect to external tools and data sources. - Antigravity includes an MCP Store where you can search for and install the service you need. - Connection details (such as a Project ID) are entered through a simple form, and credentials—passwords or IAM credentials—are stored securely. - For example, the BigQuery remote MCP server enables running queries, getting metadata, and listing resources. - BigQuery's MCP uses OAuth 2.0 with IAM for authentication and authorization, supporting Google Cloud identities. 🛠 Practical Usage - Search the MCP Store for the service you need, such as "BigQuery," and click Install to begin setup. - Enter connection details like your Project ID in the form and register credentials securely. - Once connected, ask in natural language to inspect a table's schema, then have the agent generate a script from the result. - Mix and match local-process and remote-host MCP configurations as needed. 🎯 Use Cases - Install BigQuery from the MCP Store, inspect a billing table's schema, and generate a FinOps script. - Connect to an internal database and build queries from natural-language questions. - Have the agent understand your data platform's structure via metadata and resource listings. - Build automation workflows that span local tools and external services. ⚠️ Caveats - MCP grants the agent access to external data, so least-privilege IAM scoping is a prerequisite. - Credentials are stored securely from the form, but review the permission scope of each service you connect carefully. - Service-specific auth (OAuth 2.0 / IAM) and billing terms, as with BigQuery, should be confirmed before adoption. #Antigravity# #MCP#

# Antigravity Features and Practical Usage 🚀 Stop re-explaining your usual review steps to the agent every time. Skills package that knowledge into one reusable file. 📌 Title and Feature URL Title: Skills URL: 📝 Overview Skills are reusable packages of knowledge that extend what your agent can do. A central SKILL.md file tells the agent what the skill is, when to use it, and how to execute it—so it accomplishes tasks consistently without overwhelming its context window. 🔧 How It Works The heart of a skill is a single markdown file, SKILL.md: - SKILL.md is the "brain" of the skill, describing what it is, when to use it, and how to run it. - SKILL.md is the only required file, but you can bundle additional scripts, examples, and resource directories. - Skills placed in the global scope (~/.gemini/antigravity/skills/) are available across every project on the machine. - It suits general utilities like "Format JSON," "Generate UUIDs," or "Review Code Style." - Writing a small markdown file is enough to turn a common prompt into a reusable, team-shareable command. 🛠 Practical Usage - Create a code-review skill so the agent checks for bugs, style issues, and best practices when reviewing PRs. - Capture your team's review procedure in SKILL.md and place it under ~/.gemini/antigravity/skills/ to standardize it. - Bundle scripts and examples as resources to make the steps concrete. - Keep project-specific conventions in the workspace scope and general utilities in the global scope. 🎯 Use Cases - Standardize your team's code-review bar as a code-review skill. - Make frequent small tasks—JSON formatting, UUID generation—into instantly callable skills. - Accumulate debugging playbooks as skills so anyone gets the same quality of execution. - Capture internal conventions and checklists in SKILL.md to onboard new members naturally. ⚠️ Caveats - Only SKILL.md is required, but if you don't clearly state when to use it, the agent may not fire it appropriately. - Global-scope skills affect every project, so keep narrowly useful ones in the workspace scope. - Skills are designed to save context, so scope each one tightly rather than cramming too much in. #Antigravity# #AIcoding#

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# Antigravity Features and Practical Usage 🚀 One API call to spin up an agent that writes code, runs it, and browses the web. That is what the SDK and Managed Agents make real. 📌 Title and Feature URL Title: SDK / Managed Agents URL: 📝 Overview The Antigravity SDK gives you programmatic access to the same agent harness that powers Google's products. Managed Agents, offered as a capability inside the Gemini API, let you spin up an agent in an isolated Linux environment with a single API call—where it reasons, uses tools, and executes code. 🔧 How It Works The SDK and Managed Agents turn agents into embeddable building blocks: - Isolated execution: each interaction creates a dedicated sandbox where code runs separately and safely. - Stateful, persistent environments: an environment can be resumed in follow-up calls with all files and state intact, so multi-turn work continues without reinitializing. - Custom agent definitions: you describe behavior in markdown files and can start fast from templates in the Google AI Studio Playground. - The underlying model is Gemini 3.5 Flash, optimized for concurrent execution, and integrates with the Interactions API and AI Studio. 🛠 Practical Usage - Call Managed Agents through the Gemini API to launch an agent environment in a single call. - Use the SDK to define custom agent behaviors and host them on the infrastructure of your choice. - Build on the custom agent templates in the AI Studio Playground and extend them for your use case. - Resume a prior environment to keep its files and working state across turns. 🎯 Use Cases - Spin up a sandboxed agent in one call to build a deployment-automation agent. - Embed backend workflows that involve code execution, file operations, and web browsing into internal tools. - Use stateful environments to run multi-turn research or build tasks continuously. - Host SDK-defined agents on your own infrastructure to match operational requirements. ⚠️ Caveats - Managed Agents is a Gemini API surface, so it requires an API key and a billing-enabled project. - Because each interaction creates an environment, design with sandbox lifecycle and cost in mind. - Since agents run code and browse the web, do not skip permission scoping and input validation. #Antigravity# #Agents#

# Antigravity Features and Practical Usage 🚀 No browser, no IDE—just a terminal and an agent ready to build. That is the promise of the Antigravity CLI (agy). 📌 Title and Feature URL Title: Antigravity CLI URL: 📝 Overview The Antigravity CLI brings the same agent harness that powers Antigravity 2.0 to the command line. It lets you spin up new agents instantly without a GUI, and it runs fast over SSH on remote servers or inside containers. You drive it with the agy command. 🔧 How It Works agy shares the IDE's core while adding terminal-first extensibility: - AGENTS.md at the repo root supplies shared project instructions, replacing the older .gemini/ convention with a new project-instructions system. - Agent Skills load from a global scope (~/.gemini/antigravity-cli/skills/) and a per-workspace scope (.agents/skills/). - Hooks intercept agent actions at lifecycle stages (before a tool call, after a model call, at loop stop conditions) to automate checks and formatting. - MCP servers can be configured as local processes or remote hosts to reach external data and tools. - Subagents and Plugins are also first-class extensibility features. 🛠 Practical Usage - Install on macOS/Linux with curl -fsSL | bash, or on Windows via PowerShell. - Run agy and authenticate with a Google account or a GCP project on first use. - Run agy inspect to see exactly what context is loaded: AGENTS.md, available skills, plugins, registered hooks, and connected MCP servers. - Use /goal to run autonomously, /grill-me to have the agent ask clarifying questions first, /schedule for recurring runs, and /browser to opt into browser capabilities. 🎯 Use Cases - Run agents directly from the terminal where you cannot open an IDE, such as SSH or remote sessions. - Debug "why isn't my skill firing?" or "which MCP loaded?" by inspecting the live context with agy inspect. - Launch lightweight agent work inside CI containers. - Standardize per-repo conventions by pairing AGENTS.md with shared team skills. ⚠️ Caveats - The default model is Gemini 3.5 Flash (High); preview rate limits are generous, but production workloads need a billing-enabled GCP project. - When skills or hooks misbehave, check the real loaded state with agy inspect before blaming your config. - Note that global and workspace scopes resolve skills from different directories. #Antigravity# #DevTools#

# Antigravity Features and Practical Usage 🚀 "Think before doing" with Plan, "just do it" with Fast. Match caution and speed to the nature of each task. 📌 Title and Feature URL Title: Plan / Fast モード URL: 📝 Overview Antigravity has two execution modes. Plan mode generates a detailed plan (a Plan Artifact, the Implementation Plan) before acting and implements only after approval. Fast mode skips the planning phase, interpreting your request and executing immediately. The basic split is Plan for complex tasks, Fast for minor fixes. 🔧 How It Works The two modes behave as follows: - Plan mode: it analyzes scope and examines files, then generates an Implementation Plan covering the goal, technology choices, steps, file modifications, and testing approach. It implements after approval and records changes in a Walkthrough. - Interactive approval: highlight a section of the plan and comment; the agent incorporates it before implementation begins. - Fast mode: on a "just do it" philosophy, it executes immediately without plans or waiting for approval, then reports results. - Switching modes: use the interface controls or the keyboard shortcut (Cmd/Ctrl + .); the current mode shows in the input box. 🛠 Practical Usage - For complex refactors or production-affecting changes, approve the plan in Plan mode before starting. - For minor work like typo fixes, variable renames, or boilerplate, execute immediately in Fast mode. - Combine them: do foundational work in Plan, then switch to Fast for refinements. - Mode switches apply only to new requests; ongoing tasks continue in their original mode. 🎯 Use Cases - Approve a large refactor as a plan Artifact in Plan mode, then implement safely. - Knock out adding a button or fixing a typo instantly in Fast mode. - Confirm the approach in Plan for unfamiliar tech, then move to Fast once patterns are set. - Keep oversight via Plan for changes to production or critical systems. ⚠️ Caveats - Fast has no planning phase to resolve ambiguity, so be specific and keep to a single task rather than bundling many. - Fast trades thoroughness for speed and is not suited to critical changes. - Mode switches are not retroactive; switching takes effect from the next request. #Antigravity# #AIcoding#

# Antigravity Features and Practical Usage 🚀 Check an agent's work through visible deliverables, not piles of logs. Artifacts solve the trust problem in a verifiable form. 📌 Title and Feature URL Title: Artifacts URL: 📝 Overview Artifacts are tangible deliverables that agents generate. They surface as visible outputs—task lists, implementation plans, screenshots, browser recordings, and code diffs—so you can verify the agent's logic at a glance. Instead of reading endless logs, you review the deliverable itself. 🔧 How It Works Artifacts come in several kinds with built-in feedback: - Implementation Plan: a rich markdown file covering goal, technology choices, steps, file modifications, and testing approach. - Task List: a structured breakdown of the work. - Walkthrough: a summary of completed changes and next steps. - Code diffs, screenshots, browser recordings, architecture diagrams, and images. - Commenting: like a Google Doc, you select a section and comment, and the agent incorporates your input without stopping execution. 🛠 Practical Usage - Highlight a section of a plan Artifact and leave an inline comment to steer the approach. - Use screenshots and browser recordings to confirm UI and behavior with evidence. - Review the scope of changes via code diffs before incorporating them. - Control Artifact review policies in project settings. 🎯 Use Cases - Review the plan Artifact before implementation, fine-tune via comments, then approve. - Keep a browser recording of an E2E test as proof of behavior. - Review multi-file change diffs in one place to reduce review load. - Use the Walkthrough afterward to grasp what changed and what is next. ⚠️ Caveats - Comments are applied without halting work, so give feedback early on critical changes. - Skipping artifact review turns autonomy into a lack of oversight; make review policies explicit in settings. - Browser recordings assume Chrome integration. #Antigravity# #AIcoding#