>This is taken from the following tweet: https://x.com/googleaidevs/status/2085468449145544780

We’ve put together this cheat sheet of essential slash commands to help you navigate, customize, and execute tasks faster than ever in Google @Antigravity. Whether you're setting up background tasks, tweaking your workspace utilities, or diving into deep research with subagents, these commands will help improve your daily workflow.

🔖 Bookmark this list to save time later.

Planning & Execution

• /fast: Agent will execute tasks directly. Use for simple tasks that can be completed faster.
• /goal: Run until the specified goal is completely finished.
• /grill-me: Interview me to align on a plan.
• /planning: Agent can plan before executing tasks. Use for deep research, complex tasks, or collaborative work.
• /schedule: Run an instruction on a recurring schedule or as a one-time timer.

Customizations & Rules

• /hooks: Manage hook configurations for tool events.
• /keybindings: Set custom keybindings.
• /learn: Reflect on recent successes or corrections to capture reusable skills or rules.
• /mcp: Manage MCP servers.
• /permissions: Manage tool permissions.
• /skills: List available skills.

Subagents & Tasks

• /agents: List available custom agents.
• /tasks: View background tasks.
• /teamwork-preview: Invoke a team of agents to autonomously tackle large projects. (Only available for Google AI Ultra subscription)

Workspace Utilities

• /add-dir: Add a directory to the workspace.
• /antigravity-guide: Provides a comprehensive guide, quick reference, and sitemap for Google Antigravity.
• /artifact: View and review artifacts.
• /btw: Ask a side question without interrupting the current task.
• /changelog: Show release notes and changes.
• /clear: Clear conversation and start a new one (alias: /new).
• /codesearch: Search your entire codebase with live streaming results.
• /config: Open settings panel (alias: /settings).
• /context: Visualize current context usage.
• /copy: Copy the last planner response to the clipboard (may require allowing clipboard access).
• /credits: Displays third-party software licenses and attributions.
• /diff: View uncommitted changes and per-turn diffs.
• /effort: Adjust reasoning level to balance speed and depth.
• /exit: Exit the CLI (alias: /quit).
• /feedback: Submit qualitative feedback to improve the agent.
• /fork: Create a branch of the current conversation at this point (alias: /branch).
• /help: Show available commands and keybindings.
• /logout: Log out.
• /model: Set a model.
• /open: Open a file or view opened/edited files.
• /rename: Renames the active conversation thread.
• /resume: Browse and resume past conversations (aliases: /switch, /conversation).
• /rewind: Rewind conversation to a previous message (alias: /undo).
• /statusline: Toggle the statusline.
• /title: Toggle custom terminal window title.
• /usage: View model quota usage (alias: /quota).

Get started with Antigravity: https://antigravity.google/

From one of our Mods the one and only u/soundDR

Description from the video:

In this walkthrough, we use Antigravity 2.0 to build two complete custom automations across the Chrome and local OS.

Prompt: Act as an expert Chrome Extension developer and build a Manifest V3 extension that automatically organizes unpinned tabs into native Tab Groups based on their last-accessed time. The time buckets must be exactly: Today, Yesterday, This Week, Last Week, This Month, Last 90 Days, and Archived, calculated strictly using the user's local timezone. When a tab becomes active, its timestamp should update via TTL logic and it should immediately move to the 'Today' bucket. This organization logic must be isolated per window (no cross-window tab moving) and must maintain a stable, fixed left-to-right visual sorting of the tab groups. Persist all tab timestamps using 'chrome.storage.local' to survive browser restarts and Service Worker suspension. Ensure you ignore pinned tabs entirely, and heavily debounce the 'chrome.tabs.onActivated' and 'onUpdated' listeners to prevent Ul flickering and Chrome API rate limits. Please output the complete 'manifest.json' and the 'background.js' service worker code.

Subscribe to Google Antigravity → / @googleantigravity

Google Antigravity is an agentic development platform, evolving the IDE into the agent-first era. Antigravity enables developers to operate at a higher, task-oriented level by managing agents across workspaces, while retaining a familiar AI IDE experience at its core. Agents operate across the editor, terminal, and browser, enabling them to autonomously plan and execute complex, end-to-end tasks elevating all aspects of software development.

To learn more: Website: https://antigravity.go... X: https://x.com/antigravity LinkedIn:    / google-antigravity  

Trying to figure out which workflow is lighter on background usage between the two Antigravity setups:

1. Antigravity (Standalone / Chat / CLI)
2. Antigravity IDE

For those who have tested both for similar coding tasks:

• Does using the standalone chat/CLI interface consume noticeably less context overhead than the full IDE?
• Or does the shared underlying agent core, workspace indexing, and system prompt make the background footprint pretty much identical?

All the web on one topic in the last 30 days

https://www.youtube.com/live/LcnBRo11mnk?si=Debq54kB2GbPJ-9C

Feedback? Question? Googlers are listening

u/SoundDr Thank you for everything you do for the AGY Community. We really appreciate all your hard work! Please keep up the Googler awesomeness!!!!

Hi! I saw an article about how claude code was configured to meow when it finishes working or asks for permission. This allows you to not constantly check what it is doing, because you will hear meow when you are needed. I decided to do something similar for antigravity cli, so that it sends a desktop notification and makes a sound. I managed to do this when it finishes its work using the Stop hook, but I could not when it asks for my permission. There is a PreToolUse hook, but it does not provide any information about whether agy will ask me for permission for this tool. I can write a script with hardcoded list of tools which are currently autoallowed, but I want solution that works even if I change agy permissions.

Does anyone know a way to make it send a notification only when it asks for permission to execute the tool?

https://preview.redd.it/l7smu6qnn9fh1.png?width=839&format=png&auto=webp&s=9d15347c5c279fade56383b861bb70fba44ced5a

https://preview.redd.it/yhswo1tim9fh1.png?width=586&format=png&auto=webp&s=68cda6295808c2de2a3ca15379430adb9d427ad5

As a 3-year Google AI Pro annual plan subscriber, I feel disappointed with Google for failing to provide the relatively powerful AI that was available when I first subscribed. However, since there are ways the Agy team can overcome this with their skill, I am making these proposals.

This isn't just complaining; it is something worth seriously considering.

Here are some basic facts:

1. Although it is not the most powerful AI, Qwen3.6 27B has no problems with coding.
2. Claude 4.5 Haiku is a powerful model that can easily execute instructions from Sonnet or Opus.

And Google has the Gemini 3.5 Flash-Lite model, which has capabilities similar to the two models listed above. Take a look below.

https://preview.redd.it/fekvzga2z8fh1.png?width=998&format=png&auto=webp&s=99889646a22969e98f755dea5d4cc3cef883e501

https://preview.redd.it/1ugow4liz8fh1.png?width=952&format=png&auto=webp&s=3799716e2b2bf255f9029c73e5c3169f442bc997

Regardless of benchmarks, Google's unbeatable wall lies in its multimodal capabilities. Even if tied in benchmarks, no model can surpass Gemini in actual use unless it is fine-tuned. Among publicly serviced AI models, Gemini is like a god in multimodal capability.

https://preview.redd.it/ktkmlyvh09fh1.png?width=975&format=png&auto=webp&s=44e5985d116364aca2bbb19cf2693520d422b735

Now, here are the proposals:

• Add support for 3.5 Flash-Lite in Agy-Cli.
• Give system prompts to the 3.1 Pro model or 3.6 Flash. The current system prompt seems to be around 8k, but it doesn't matter if it gets larger. Ultimately, 3.5 Flash-Lite will reduce overall costs for both users and Google while increasing satisfaction.
• Utilize 3.5 Flash-Lite as a multi-agent for the following tasks: tasks that do not require reasoning, coding with clearly structured plans, tasks requiring online research, document summarization, and codebase investigation and organization—actively leverage 3.5 Flash-Lite for high-frequency, shallow-level tasks.
• Assign simple yet clear personas and explicit prompts to sub-agents.
• Allow users to utilize their chosen model for deep-dive tasks. If they selected 3.6 Flash Low, use that; if they selected 3.1 Pro High, use that.
• If a user is dissatisfied with the work result even on the third attempt, escalate to a higher-tier model than the one initially used.

Of course, you shouldn't just paste this into the system prompt, and you'll need to think about it further. But the concept is this:

When given clear instructions, smaller models perform tasks extremely well—not quite as perfectly as high-performance models, but enough to satisfy users.

Also, I don't know how strictly Agy-Cli limits the max output tokens of models or how small the limit for document generation is set, but its document generation capability is at a pathetic level. Ease this restriction by about 5 times.

and store text communications between agents locally on the individual computer where Agy is installed.

If 3.6 Flash or 3.1 Pro create a Todo list and actively utilize 3.5 Flash-Lite as parallel sub-agents, you can produce better results at a lower cost. If someone is an x20 user, it might be nice to add an option to use the 3.6 Flash model as a sub-agent, but I guarantee that for most users, 3.5 Flash-Lite conducting large-scale research will be more than enough. While it's fine for Flash or Pro alone to research from backend to frontend and do reasoning, it's expensive. It's more efficient to variously utilize cheaper sub-agents and apply the high intelligence of Pro or Flash to subsequent tasks.

For reference, I completely avoided using 3.1 Flash-Lite on the web because its result quality was too poor. But now, I use 3.5 Flash-Lite quite a bit because it's fast and suitable for simple tasks—I was doing it without even realizing it. That's why I looked up benchmark results again. Originally, I was only interested in 3.6 Flash to find a more powerful model and never even thought about 3.5 Flash-Lite, but I realized that what matters in this update isn't 3.6 Flash.

Frankly speaking, Google does not have a single impressive AI model at this point. However, 3.5 Flash-Lite is the most impressive model among them, and if Agy actively utilizes this model, it will dramatically improve work completion quality.

The team developing the AI models failed, but the Agy team can make up for it.

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Supplementary explanation for this Text.
To help you understand the text, I am adding 4 screenshots.

example 1

Simple prompt

Prompt to utilize sub-agents

example 2

Simple prompt

Prompt to utilize sub-agents

Having a feature available and having it run automatically are different things. For example, what makes Opus excel is running several geniuses of its own caliber in parallel to gather vast amounts of information, which is why it is expensive.

From the perspective of Agy's developers, they might think, "Why don't you just type in the prompt yourself?" but from a user's perspective, it's not like that.

In the case of a simple prompt, a single Gemini can do the searching, analysis, modification, and even testing all by itself, but it is expensive.

In the case of the 'Prompt to utilize sub-agents', there is a cost to creating sub-agents, but by using 3.5 Flash-Lite—which costs 1/6 of Flash—to find and organize the directory structure, source code files, and framework stack, you can have it investigate more than Flash alone could, at a very high speed and low cost. Following a plan made by an expensive model, the costly coding process can also be investigated via the inexpensive 3.5 Flash-Lite, while 3.6 Flash can directly review it or just create test files. Then, 3.5 Flash-Lite can execute the test files and organize the results.

As we all know, this is no longer an era where we make a single model do all the work. It is an era where the main model controls other AIs, while sub-agents utilize skills (pre-written prompts) to bring about better results.

And one of Gemini's major strengths compared to other models is its blazing-fast token generation capability. Whenever needed, it is easy to create a persona for a sub-agent and spawn an inexpensive sub-agent with that persona.

If you look up reviews on Gemini 3.6 Flash, you'll see. Were the reviews good? Most of it is mockery. That is the market's assessment.
With an inexpensive yet excellent model like 3.5 Flash-Lite, actively utilizing it will save the main AI's context while producing better results at a lower cost.

You shouldn't think, "What can't Agy and Gemini build right now that makes them ask for this?"
This is a post about how to build better results at a lower cost.
And these are also things that the Agy team can fix without putting in major efforts like LLM training.

I wanted to tell you guys about a very different use case I have for the anti-gravity CLI today

So I was hired to install the Wi-Fi in an Airbnb. Pretty simple setup but it is included taking their existing router, turning it into bridge mode, and then running everything through another router, a POE plus gigabit switch and then out to two wireless access points.

It's been a while since I've done any actual technical stuff like that so I was pretty rusty.

So what I did was I grabbed an old laptop of mine, installed Linux Mint, and installed the antique gravity CLI.

I made a project folder of documents that I had typed up before, like hardware we were using, the direction the cables were going to have to be buried in, where it was, why it was going to be used and stuff like that

Running it from that folder, I told it what we were doing and I had it download all of the quick start guides, all the documentation for the hardware, headed organize the plan a little bit better and then I had it download every network CLI tool it thought would be useful. Like wireshark CLI and a couple other packet sniffing tools and management tools.

It was basically my man on the inside configuring the network from within.

I'm not done with it yet but it has been really really really helpful. It's recommendations for how to configure the router for long-term support, for example, we're pretty interesting. And I was only using 3.5 flash high.

When I started getting the router and the switch plugged in and everything hooked up it was even able to figure out that one of the pins on one of the network cables was not working.

It was just so fascinating to see how it coalesced all of the usage from the documentation with the official guides from the hardware and combined with all of the CLI tools it was using.

Very cool, highly recommended

I’m a corporate user of Claude Code and Cursor.
Yesterday I was developing my side project with agy cli and I’m impressed. I was adding feature to the app and usually switched to YouTube to watch something while AI tool do something. I saw in a 30 sec that my feature was added and I was very skeptical.

I’ve opened the app and I saw it’s working like I described, shocked. It was adding packages, rewriting UI, adding backend, pushing cloud build to server and ofc run some linting and test.

It was so fast that blew my mind, Claude code in my work is used with google vertex and it’s so slow that I can literally go take a shower and come back to not finished task.

Btw. I’m using 3.1 pro with high effort.

Thank you google, agy cli is getting really better, I should say competitive to Claude code.

Opinions on Google Open Knowledge Format in markdown? Is someone using it?

AGY Builders: Here is another great example of how amazing it is to build projects with Antigravity CLI.

🚀 Google Sunsets Gemini CLI – Enter "Antigravity CLI"

Google has officially discontinued the Gemini CLI, replacing it with a new command-line interface tool called Antigravity CLI (or agy). The creator behind the Creator Magic channel provides a firsthand look at this transition, exploring its features, pitfalls, and integration into modern development workflows.

Key Takeaways:

• The Switch: Gemini CLI is no longer supported. Google is pushing developers toward the new agy tool, which is installed via a simple terminal command.
• Closed Source Concerns: Unlike the previous Gemini CLI, which was open-source, Antigravity CLI appears to be closed-source, which the creator notes is a major shift in direction for Google's tooling.
• Multi-Model Support: One of the standout features is that agy isn't restricted to just Google's models. It provides access to various models, including Gemini 3.5 Flash, Claude Opus, and GPT-4o, making it a potentially versatile utility.
• Workflow Integration: The host demonstrates how they integrated Antigravity CLI into their own framework, Tank, which allows for a "multi-agentic" approach—essentially chaining different AI agents (like Claude Code, CodeEx, and Antigravity) to build and refine projects automatically.
• The "Quota" Trap: A major point of discussion is the strict usage limits on the free tier. Users have reported getting walled off after just 5–6 prompts. The creator notes that this is particularly confusing for Google Workspace users, as standard subscriptions often don't grant extra CLI usage, leaving developers hitting strict caps quickly.

Creator's Perspective:

The creator experiments with building a functional "Epic Snake" game using Antigravity CLI and discusses the "endgame" vision: a system where AI agents work in loops, handing off tasks to one another, switching providers to manage token usage, and ultimately self-improving code without needing constant human intervention.

While the multi-model access is a power move, the consensus from the stream is to approach the free tier with caution due to the aggressive rate limits, especially for those looking to build complex projects.

Watch the full deep dive here: https://www.youtube.com/live/kjFi1IuzWY4

---

What are you building today?

Update: Forgot to mention that to be fair and support `opencode` I plan to upgrade to their $10 /month plan.

I have successfully setup a combo of Antigravity + Opencode + Local LLM but I still believe there is a room for improvement. Please share if possible:

1. Ideas/tips to help me improve.
2. How much this setup is helping me save cloud tokens? Since we don't know the exact Google AI Pro qu*ta in absolute terms.

Details are below:

1. Setup MCP customization in Antigravity on Google AI Pro plan to share coding workload with Opencode (free plan) and Local LLM specifically the donkey work like searching and exploring local files, scanning logs, etc.
2. I am using Ubuntu WSL on Windows 11 to run Qwen 3.6 via LLAMA.cpp with Opencode and Antigravity installed in Windows 11.

Most recent task:

Here is a summary of the token usage and division of labor:

1. Division of Labor

* Antigravity (Cloud - Google AI Pro): Acted as the Manager. Architected the solutions, researched repository structures, generated the revised implementation/verification plans, and wrote detailed orchestration instructions.

* OpenCode (Local Agent): Acted as the Implementer. Executed the multi-file coding task to modify flow.js and flow_panel.html under explicit manager guidelines.

* Local LLM (ask_local - Qwen 32k on RTX 3090): Acted as the Inspector. Used for git commit analysis, file content analysis, and log reviews, keeping mechanical token queries off the cloud.

2. Approximate Token Breakdown

* Antigravity (Cloud): ~18,000 input / ~2,500 output tokens (high-level reasoning & planning).

* OpenCode (Local): ~15,000 local tokens (code modifications and git staging).

* Local LLM (Local): ~12,000 local tokens (inspecting codebases, git history, and summaries).

By routing code analysis and implementation to ask_local and ask_opencode, nearly 60% of the total token volume was processed locally on your RTX 3090 instead of consuming cloud qu*tas (`f**k mods for blanket filtering this word`).

Monitor running in WSL:

https://preview.redd.it/1hdh13u61a8h1.png?width=1193&format=png&auto=webp&s=0564c8203622c0acfe41d09a858c85f6e871c254