Google Antigravity Skills vs. Claude
The choice between Google Antigravity Skills and Claude (specifically via the Model Context Protocol or MCP) depends entirely on your specific use case: Google Antigravity Skills are the superior choice for deep, local codebase integration and high-speed “Vibe Coding” loops, whereas Claude remains the better general-purpose assistant for cross-platform administrative tasks. Google Antigravity Skills allow developers to “hard-code” proprietary logic, local CLI tools, and internal database queries directly into the IDE with near-zero latency, effectively transforming the AI into a specialized employee who understands your unique stack. In contrast, Claude’s MCP excels at connecting your AI to the broader digital world—such as Slack, Google Drive, and Notion—making it an ideal tool for summarizing communications and managing general documentation. For startups looking to automate “tribal knowledge” and technical debt within a repository, Google Antigravity is the clear winner.
Understanding the Shift: From Prompting to Teaching
For years, developers have treated AI as a chatbot where the workflow involves constant explaining. Every time a developer starts a new task, they find themselves pasting API documentation, explaining legacy database schemas, and reminding the AI which custom wrappers are used for deployments. This repetitive cycle, often referred to as the “Groundhog Day” of software development, wastes hours of productive time and introduces friction into the coding process.
Google Antigravity Skills represent a paradigm shift in this relationship by moving from “Prompting” to “Teaching.” Instead of asking an AI to guess how your system works, you define a Skill that grants the IDE permission to execute code within your environment. This means the AI transitions from a passive observer to an active agent that runs your own internal functions to fetch the exact context it needs, instantly and accurately.
By teaching the IDE your internal APIs once, you ensure that the AI remembers them forever. This institutional memory is stored directly in your codebase, allowing the AI to act as a runtime environment rather than just a text generator. The result is an AI that grows more intelligent and specialized the longer it stays within your specific repository.
What Are Google Antigravity Skills?
At their most fundamental level, Google Antigravity Skills are TypeScript definitions and Markdown instructions that give your AI permission to execute scripts and tools on your local machine. This is a radical departure from standard AI assistants that are confined to a “sandbox” or a text-only chat window. Antigravity Skills turn the AI into a functional agent with a toolbox containing your own proprietary scripts.
Without Skills, if you ask an AI how to query your user database, it might provide a generic SQL template that lacks the necessary security headers or correct table names. With a Skill defined, the same request triggers the AI to execute a specific internal function, such as fetchUser(), which returns live, accurate data formatted exactly for your needs. This capability bridges the gap between AI hallucinations and real-world execution.
These Skills are modular and file-based, meaning they live inside your repository in a dedicated directory (usually .agent/skills/). This structure ensures that the logic follows the code; when a new developer clones the repo, the AI already “knows” how to interact with the system because the Skills are part of the version-controlled codebase. It effectively solves the “Bus Factor” by ensuring technical knowledge is encoded for the AI to use.
The Showdown: Key Differences at a Glance
When comparing Google Antigravity Skills to Claude’s Model Context Protocol (MCP), several critical distinctions emerge regarding focus, latency, and context. While both technologies aim to extend AI capabilities, they serve very different niches within a developer's daily routine.
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Primary Focus: Claude is designed for a “General Purpose” workflow, focusing on connecting to the outside world. It is excellent for pulling data from Slack, Google Drive, or Notion to help with high-level planning. Google Antigravity, however, is built for “Deep Code Integration,” specifically connecting your IDE to your local database, specific repository files, and CLI tools.
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Execution Latency: Because Claude’s tool use often requires round-trips to an API server, the latency is relatively high. This is acceptable for chat-based tasks but can feel sluggish during intense coding loops. Google Antigravity Skills run locally inside the Antigravity engine, resulting in “Near-Zero” latency and instant execution for debugging and script running.
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Contextual Depth: Claude has a “Broad” context, meaning it is great at summarizing a PDF or a Slack thread. Google Antigravity has a “Proprietary” context, allowing it to understand the nuances of your specific migration scripts or the health status of your staging environment.
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Verdict for Startups: Google Antigravity is the clear winner for automating startup workflows where the goal is to ship code faster and reduce the burden of internal documentation.
The “Librarian” Skill: Solving Context Limits
One of the most persistent problems in AI-assisted development is the context window. Even with modern advancements, an AI often struggles to remember the nuances of a custom internal library, leading it to hallucinate generic code (like Tailwind classes) that your project doesn't actually use. The “Librarian” Skill is a specialized extension designed to solve this by providing the AI with a direct link to your internal documentation.
By exposing a function like read_internal_docs(component_name), you allow the AI to search your internal Wiki or Storybook files the moment a question arises. Instead of the developer having to find and paste the documentation, the AI acts as its own researcher. It fetches the exact specifications for your internal UI kit before it writes a single line of code, ensuring the output is perfectly compliant with your team's standards.
This Skill is particularly transformative for junior developers. When a new hire asks the IDE to generate a “Button” component, the AI doesn't just guess; it checks your specific component library, sees that you use a custom <AppButton> wrapper with specific props, and provides code that actually compiles and follows your design system.
The “Ops” Skill: Automating Fear and Safety
Deployment anxiety is a real issue for engineering teams, especially for junior developers who are terrified of breaking the production environment. They often wait for a senior developer to manually check monitoring dashboards before they feel safe merging a pull request. The “Ops” Skill automates this “Fear” by wrapping your monitoring CLI—such as Datadog, Sentry, or a simple health check script—into a Skill.
Once configured, a developer can simply ask the IDE, “Is it safe to merge?” The AI then executes the check_staging_health() Skill, which queries your monitoring tools and returns a real-time status report. If the system is “Red” due to high latency or error rates, the AI will warn the developer and provide the logs, effectively acting as an automated safety officer.
This proactive approach significantly reduces the “Review Bottleneck.” By the time a senior developer looks at a PR, they can see that the AI has already verified the health of the staging environment. This builds a culture of safety and allows the team to deploy with much higher confidence and frequency.
The “Data” Skill: Live Debugging and Troubleshooting
Traditional debugging often involves a “context switching” nightmare. A developer might need to log into three different dashboards—a database client, a logging service, and an admin panel—just to find a specific User ID or verify a data anomaly. The “Data” Skill eliminates this friction by giving the AI read-only access to specific database queries via your internal APIs.
With a “Data” Skill such as fetch_user_debug_info(email), you can ask Antigravity a question like, “Why is this specific user getting a 500 error?” The AI doesn't just look at the code; it fetches the actual data for that user, identifies a malformed field or a missing relationship in the database, and points out the anomaly instantly.
This type of live debugging turns the IDE into a powerful diagnostic tool. Because the AI can “see” the data and the code simultaneously, it can spot logic errors that would take a human developer significantly longer to find. This integration of live data into the development environment is perhaps the most significant productivity gain offered by Google Antigravity over traditional chat-based models.
Technical Tutorial: Building Your First Antigravity Skill
Building a Skill is a straightforward process that involves defining a folder structure and writing instructions for the AI. To illustrate this, we can look at the creation of an “Ops Safety” Skill that checks system health before allowing a deployment.
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Step 1: Create the Directory: Antigravity looks for skills in the
.agent/skills/directory of your project. You should create a folder for your skill, such asops-safety, and include ascriptssubfolder for the actual execution logic. -
Step 2: Create the Execution Script: You need a “Black Box” script—a simple Bash or Python script that returns a clear signal. For a health check, this might be a script that pings your monitoring API and outputs a status code like “200 OK.”
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Step 3: Define the Logic in
SKILL.md: This is the most critical file. It contains a YAML frontmatter with a description that acts as the “trigger” for the AI. Below the metadata, you write a “Decision Tree” in Markdown to tell the AI how to handle the script's output. -
Step 4: Testing the Result: Once the files are saved, the Skill is active. When you ask, “Is it safe to merge?”, the AI will automatically load the instructions, execute your script, and provide a clear “✅” or “🛑” signal based on the result.
The Anatomy of the SKILL.md File
إن SKILL.md file serves as the “brain” of the Antigravity Skill. It bridges the gap between the user's natural language request and the technical execution of a script. This file uses a combination of metadata and structured instructions to ensure the AI behaves predictably and safely.
The metadata at the top of the file (YAML frontmatter) is what the Antigravity engine uses to index the Skill. It includes a name and a description. The description is particularly important because it tells the AI when to use the skill. Phrases like “Use this whenever the user asks ‘Is it safe to merge?'” act as semantic triggers that prompt the AI to look at this Skill for the answer.
Inside the Markdown body, you define the “Safety Protocol” or “Rules of Engagement.” This often includes a step-by-step Decision Tree. By explicitly telling the AI to “IF output contains ERROR -> Tell the user: Do not merge,” you prevent the AI from making subjective guesses. This level of control is what makes Google Antigravity a professional engineering tool rather than a generic consumer chatbot.
Comparison Table: At a Glance
| Feature | Claude Skills (MCP) | Google Antigravity Skills |
| Primary Focus | General Purpose / Admin / Communications | Deep Code Integration / Repo Automation |
| Connectivity | Slack, Google Drive, Notion, Browser | Local Repo, Local DB, CLI, Scripts |
| Latency | High (Cloud-based API rounds) | Near-Zero (Local execution) |
| Contextual Range | Broad (Web and Cloud documents) | Proprietary (Internal code and live data) |
| Best For | General Assistants & Planning | Automating Startup Workflows |
| Execution | Remote / Server-side | Local / Inside the Antigravity Engine |
Conclusion: Skills as Business Assets
For modern software teams, the real barrier to scaling isn't necessarily hiring more people; it's the efficient transfer of knowledge. Every time a senior developer leaves a company, they take a piece of “Tribal Knowledge” with them. Google Antigravity Skills provide a way to capture that knowledge and store it as a permanent asset within the codebase itself.
By writing an Antigravity Skill, you are ensuring that even if your lead engineer is unavailable, the AI knows exactly how to run the build script, check the database, or verify a deployment. This transforms your repository from a collection of code into an “Intelligent System” that is capable of managing and explaining itself to both humans and machines.
Ultimately, you should use Claude to summarize your emails and manage your general documentation, but use Google Antigravity Skills to fix your production database and ship your product. As the AI space continues to evolve toward “Agentic” workflows, the teams that encode their expertise into Skills will be the ones that dominate the market in 2026.
