I Tested Google Antigravity Skills for 3 Weeks

The Problem: We’re All Stuck on the Prompt Treadmill

Here’s what my typical morning looked like before Skills:

1. Open Antigravity
2. Paste my coding standards (450 tokens)
3. Explain my project architecture (280 tokens)
4. Remind it about our naming conventions (150 tokens)
5. Finally start building

Tomorrow? Repeat steps 1-4.

I measured this. Over one week, I spent 6.5 hours just re-explaining context that should have been remembered.

But here’s the real problem: AI assistants don’t just forget your preferences—they forget how to think systematically.

Without structure, debugging becomes a casino game. The AI throws random solutions at symptoms instead of diagnosing root causes. I watched Claude suggest the same fix three times in a row with slight variations, completely stuck in what I call the “hallucination loop.”

If you’re managing multiple AI tools for different tasks, check out our ClickUp vs Monday comparison to see how project management tools can help organize these workflows.

Enter Skills: The Architecture That Changes Everything

Skills aren’t just saved prompts. They’re executable automation.

Think of it this way:

Traditional AI = Consultant
You explain the problem. They think about it. They propose solutions. You explain again tomorrow.

Skills-Powered AI = Factory
You trigger the machine. It executes the programmed workflow. Same perfect result every time.

Here’s a concrete example from my testing:

Before Skills: Random Walk Debugging

Me: "This code is throwing an error"

AI: "Try adding a .catch() handler"
[Fails]

AI: "Try using async/await instead"
[Fails]

AI: "Try wrapping in try/catch"
Me: "That's literally the same thing you just suggested!"

[15 minutes wasted, problem still not solved]

After Skills: Systematic Debugging

Me: "This code is throwing an error"

AI: [Activates troubleshooter skill]

AI: "Step 1 - Analyzing log:
- Error: 401 Unauthorized from api.service.com/v2/data
- Token present: Bearer eyJ...
- Timestamp: 2025-01-15 14:32:18 UTC

Step 2 - Root cause:
Token is for v1 endpoint but you're calling v2. API version mismatch.

Step 3 - Fix:
Updating token generation to include v2 scope..."

[Problem solved in 90 seconds]

The difference? The skill enforces a mandatory workflow: analyze log → identify root cause → propose fix. No random solutions. No hallucinations.

The Results: I Measured Everything

I’m a data guy. Every claim needs backing. Here’s what I measured over three weeks across 25 projects:

Token Efficiency

• Before Skills: 450 tokens per request (instructions + context)
• After Skills: 180 tokens per request (60% reduction)
• Monthly savings: ~50,000 tokens = significant cost reduction

Time Savings

• Week 1: 3.5 hours saved using 4 base skills
• Week 2: 6 hours saved (added 3 custom skills)
• Week 3: 9.5 hours saved (10 skills total)
• Projected monthly: 38+ hours

Quality Improvements

• Failed implementations: Down 40% (from 15 to 9 across 25 tasks)
• Major rewrites needed: Down 67% (from 9 to 3)
• First-try debugging success: Up from 31% to 73%
• Hallucination loops: Down from 18 instances to 2

The Four Skills That Changed My Workflow

Let me show you the four skills that delivered these results:

Me: "Create a skill for competitive analysis that scrapes pricing pages 
and generates markdown reports"

AI: [45 seconds later]
"Skill created at ~/.gemini/antigravity/global_skills/competitive-analysis/
Includes: Python scraping script, markdown template, validation workflow"

Me: "What are the top discussions in r/SaaS this week?"

AI: [Executes script, 3 seconds later]
"Here are the top 10 posts:
1. 'Hit $10K MRR after 14 months' (847 upvotes)
2. 'Stripe vs Paddle comparison' (623 upvotes)
..."

How Skills Actually Work: The Architecture

Understanding the architecture helps you build better skills.

Every skill has three components:

The Secret: Progressive Disclosure

Skills use a three-stage loading process:

1. Discovery: AI scans all skill names/descriptions
2. Activation: If relevant, reads SKILL.md
3. Execution: Loads specific resources or runs scripts

This is what keeps token usage minimal. Instead of loading 800 lines of documentation, the AI loads only the 50 lines it needs.

The Compound Effect: What Happens Over Time

Here’s what I observed over three weeks:

• Week 1: Install 4 base skills → Save 3.5 hours
• Week 2: Build 3 custom skills for my domain → Save 6 hours total
• Week 3: 10 skills covering common workflows → Save 9.5 hours total
• Projected Month 3: 25+ skills → Save 15+ hours weekly

But time savings aren’t the only benefit:

• Consistency: Every code review follows the same checklist
• Quality: Fewer bugs make it to production
• Onboarding: New team members use the same skills
• Knowledge preservation: Best practices encoded in skills, not lost when people leave

Skills vs. Claude Code: Why They’re Different

People ask me: “Aren’t Claude’s skills the same thing?”

No. Critical difference:

Claude Code Skills = Knowledge
The AI reads the skill like a cookbook, then executes the recipe itself.

Antigravity Skills = Knowledge + Automation
The skill can contain executable code that runs directly.

Think of it this way:

• Claude: Chef reads recipe → cooks meal
• Antigravity: Pre-made dish → heat and serve

Both have their place. But for repeated tasks, the factory pattern (Antigravity) is faster, more reliable, and more efficient.

Getting Started: Your First Steps

If you want to try this yourself:

I’ve documented everything I learned—the successes, the failures, the exact workflows that work—in a comprehensive guide with all four production-ready skills included.

The Bottom Line

Three weeks ago, I spent 6.5 hours per week re-explaining context to AI.

Today, I spend zero minutes. The skills know.

Three weeks ago, debugging was a gamble. Random solutions, hallucination loops, wasted time.

Today, it’s systematic. Root cause → targeted fix. 73% first-try success rate.

Three weeks ago, I wrote the same automation scripts over and over.

Today, they’re skills I invoke with a sentence.

This isn’t about using AI more. It’s about using it smarter.

Google Antigravity Skills turn your AI assistant from a forgetful consultant into an automated factory. The difference is measurable, significant, and compounds over time.

If you’re serious about building faster without sacrificing quality, this is the architecture to learn.

Your move.