Google Antigravity Skills: Stop Explaining Your Codebase to AI

Every day, your team wastes hours explaining your own company to a robot.

You paste API docs. You explain the legacy database schema. You remind the AI for the tenth time that you use a custom wrapper for deployments.

It’s the "Groundhog Day" of software development.

Google Antigravity just ended this cycle with a new feature called "Skills."

Unlike standard prompting, Skills allow you to "hard-code" your startup’s proprietary logic directly into the IDE. It transforms your AI from a generic coding assistant into a specialized employee that knows your stack better than you do.

Here is why Antigravity Skills beat Claude’s MCP, and how to use them to automate your "Tribal Knowledge."

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The Paradigm Shift: Prompting vs. Teaching

Most developers treat AI like a chatbot: You ask, it guesses. Antigravity Skills treat AI like a runtime environment: You define, it executes.

According to the official docs, a "Skill" is simply a TypeScript definition that grants the IDE permission to run code.

• Before Skills: You paste your internal documentation into the chat window.
• After Skills: The AI runs read_internal_docs() and fetches the exact context it needs, instantly.

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What Are Google Antigravity Skills?

(And why they are more than just "Prompts")

At its core, a "Skill" is a TypeScript definition that grants your AI permission to execute code within your environment.

Most developers are used to treating AI like a chatbot: you ask a question, it gives text back. Antigravity Skills turn the AI into an Agent. You give it a toolbox.

• Without Skills: "How do I query our user database?" (AI gives a generic SQL example that fails).
• With Skills: "Find user [email protected]." (The AI executes your internal fetchUser() function and returns real live data).

This shifts the paradigm from "Prompting" to "Teaching." You teach the IDE your internal APIs once, and it remembers them forever.

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The Showdown: Google Antigravity Skills vs. Claude (MCP)

If you have been following the AI space, you might ask: "How is this different from Claude’s Model Context Protocol (MCP) or Tool Use?"

While both technologies allow AI to use tools, their implementation in a startup environment is radically different.

Feature	Claude Skills (MCP)	Google Antigravity Skills
Primary Focus	General Purpose. Connecting Claude to the outside world (Google Drive, Slack, Notion).	Deep Code Integration. Connecting the IDE to your specific repo, local database, and CLI tools.
Latency	High. Requires round-trips to the API server. Good for chat, slow for coding loops.	Near-Zero. Runs locally inside the Antigravity engine. Instant execution for debugging and scripts.
Context	Broad. "Read this PDF," "Summarize this Slack thread."	Proprietary. "Run our specific migration script," "Check our staging health."
Verdict	Great for General Assistants.	The clear winner for Automating Startup Workflows.

The Takeaway: Use Claude to summarize your emails. Use Antigravity to fix your production database.

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3 Ways to Teach AI Your Proprietary Codebase

For a startup founder or lead dev, the goal is to reduce the "Bus Factor." Here are three specific Skills you should build immediately to capture your team's knowledge.

1. The "Librarian" Skill (Solving Context Limits)

• The Problem: Your AI doesn't know how to use your internal MyCompany-UI-Kit. It keeps hallucinating Tailwind classes you don't use.
• The Skill: read_internal_docs(component_name)
• How it works: You expose a function that lets the AI search your internal Wiki or Storybook.
• The Result: When a junior dev asks for a "Button," the AI fetches your specific documentation first, then writes code that actually compiles.

2. The "Ops" Skill (Automating Fear)

• The Problem: Junior developers are terrified of breaking things during deployment. They ask senior devs to check everything manually.
• The Skill: check_staging_health()
• How it works: You wrap your monitoring CLI (Datadog/Sentry) in a Skill.
• The Result: A developer asks the IDE: "Is it safe to merge?" The AI runs the check, verifies the system is green, and gives the go-ahead.

3. The "Data" Skill (Live Debugging)

• The Problem: Debugging a customer issue involves logging into three different dashboards to find a User ID.
• The Skill: fetch_user_debug_info(email)
• How it works: A secure, read-only function that queries your production replica.
• The Result: You simply ask Antigravity: "Why is User X getting a 500 error?" The AI pulls their real data and spots the anomaly instantly.

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Tutorial: Build the "Ops" Skill (From Example #2)

Let's take the "Ops Skill" mentioned above and build it for real. We will teach the AI how to verify system health before allowing a deployment.

According to the official documentation, we need to create a specific folder structure.

Step 1: Create the Directory

Antigravity looks for skills in the .agent/skills/ directory. Create a folder named ops-safety and a scripts subfolder.

.agent/skills/
└─── ops-safety/
    ├─── SKILL.md                 <-- The Instructions
    └─── scripts/
         └─── check_staging.sh    <-- The "Black Box" Script

Step 2: Create the "Black Box" Script

We want the AI to execute a specific check, not read our entire DevOps codebase. We create a wrapper script that returns a simple "Green" or "Red" signal.

File: .agent/skills/ops-safety/scripts/check_staging.sh

#!/bin/bash
# Simulates checking Datadog/Sentry status
# Usage: ./check_staging.sh

echo "Connecting to Staging Monitor..."
# In real life, this would curl your monitoring API
echo "STATUS: 200 OK"
echo "ERROR_RATE: 0.01%"
exit 0

(Make sure to run chmod +x on this file)

Step 3: Define the Logic (SKILL.md)

Now, we write the instructions. We use the YAML frontmatter to define the trigger keywords, and a Decision Tree to enforce safety rules.

File: .agent/skills/ops-safety/SKILL.md

---
name: ops-safety
description: Validates staging environment health. Use this whenever the user asks "Is it safe to merge?" or "Can I deploy?".
---

# Deployment Safety Protocol

You are the safety officer for this repository. You must verify the staging environment health before answering deployment questions.

## When to use this skill
- When the user asks "Is it safe to merge?"
- When the user asks "Check staging status"
- Before generating any `git push` commands

## Decision Tree (Follow Strictly)

1. **Run the Health Check**
   - Execute the script: `./scripts/check_staging.sh`

2. **Analyze Output**
   - **IF** output contains "STATUS: 200 OK":
     -> Tell the user: "✅ Staging is Green. Proceed with merge."
   - **IF** output contains "ERROR" or high latency:
     -> Tell the user: "🛑 Staging is unstable. Do not merge."
     -> Display the error logs from the script.

## Style Guide
- Keep responses brief.
- Use emojis (✅/🛑) to clearly signal safety status.

Step 4: The Result

Once saved, the "Ops Skill" is active.

User: "Hey, is it safe to merge this PR?"

Antigravity Agent:

Reading ops-safety skill...Executing ./scripts/check_staging.sh...

"✅ Staging is Green. Status: 200 OK Error Rate: 0.01%

You are clear to merge."

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Conclusion: Skills Are Business Assets

For small teams, the barrier to scaling isn't hiring—it's knowledge transfer.

Every time you write an Antigravity Skill, you are saving "Institutional Memory" into your codebase. You are ensuring that even if your Lead Engineer is on vacation, the AI knows how to run the build script.

Don't let your AI stay a junior developer forever.

Ready to automate your busy work? Dive into the Official Antigravity Docs and start building your first specialist agent today.