Google Antigravity Now Supports Agent Skills: Easily Extend Your AI Agents with Reusable Knowledge Packs

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Google Antigravity’s Agent Skills feature lets you extend AI agent capabilities using an open standard. Place a SKILL.md file (with YAML frontmatter and detailed instructions) inside .agent/skills/ for project-specific workflows or ~/.gemini/antigravity/skills/ for global reuse. Agents automatically discover skills at conversation start, evaluate relevance via the description, and apply full instructions when appropriate—delivering consistent, repeatable behavior without repeated prompting.

Have you ever found yourself typing the same detailed instructions into your AI coding assistant over and over again?

Things like:

• “Always follow our team’s pytest style and aim for >85% coverage”
• “Review code using this exact 7-point security & performance checklist”
• “Structure API responses with these specific error codes and logging patterns”

It gets tiring fast. Google Antigravity’s newly released Agent Skills feature solves exactly this problem.

Announced recently, Agent Skills bring a clean, standardized way to package reusable knowledge so your agents become dramatically more consistent and “aware” of your preferences—without you having to re-explain every time.

Let’s walk through what Agent Skills are, how they work, how to create them, and the practical patterns that make them truly powerful.

What Are Agent Skills in Google Antigravity?

Agent Skills are an open standard (originally from Anthropic and now widely compatible) for giving AI agents structured, reusable instructions.

A skill is simply:

• A folder
• Containing (at minimum) one file: SKILL.md

That Markdown file has two main parts:

1. YAML frontmatter — metadata the agent reads first
2. Detailed human-readable instructions — what the agent should actually do when the skill is relevant

When you start a new conversation in Antigravity, the agent receives a concise list of all available skills (only their name + description). If your current task matches a skill’s description, the agent reads the full SKILL.md and follows it closely.

This “progressive disclosure” design keeps context efficient: the agent doesn’t load hundreds of pages of rules upfront—only what matters right now.

Two Scopes: Workspace-Specific vs Global

Antigravity gives you two natural places to store skills, each with clear trade-offs:

Pro tip: Start skills inside your project (.agent/skills/). Once they prove valuable across multiple repos, move (or symlink) them to the global folder. This keeps experimentation safe and avoids polluting every project early.

Minimal Skill Structure Example

.agent/skills/code-review/
└── SKILL.md

---
name: code-review
description: Reviews code changes for bugs, style violations, security issues, and performance anti-patterns. Use when asked to audit PRs, check code quality, or evaluate implementations.
---

# Code Review Skill

When reviewing code, follow this mandatory checklist in order. Cover every point explicitly in your response.

## Mandatory Review Checklist

1. Correctness — Does the code actually implement the stated requirement?
2. Edge Cases — Are null/empty/max/min/concurrent/failure states handled?
3. Style & Conventions — Matches project’s established patterns (naming, imports, formatting)?
4. Performance — No obvious quadratic behavior, unnecessary allocations, or lock contention?
5. Security — Injection risks, hardcoded secrets, unsafe deserialization?
6. Maintainability — Magic numbers extracted? Complex logic split into small functions? Adequate comments/tests?

## Feedback Rules

- Be specific: quote file + line + problematic code
- Always explain *why* something is an issue (crash risk, readability debt, attack vector, etc.)
- Suggest concrete alternatives or refactored snippets whenever practical
- Prioritize: critical (security/crash) → medium (perf/concurrency) → low (style/readability)

After saving, start a fresh chat and paste some code with “review this PR diff” — most of the time the agent will activate the skill automatically.

Recommended Folder Structure (Optional but Powerful)

While only SKILL.md is required, adding companion files makes skills far more capable:

.agent/skills/python-testing/
├── SKILL.md
├── scripts/
│   └── run-coverage.sh
├── examples/
│   └── ideal_test_example.py
└── resources/
    └── pytest.ini.template

Inside SKILL.md you can tell the agent:

“

“If coverage is requested, first run ./scripts/run-coverage.sh --help to understand options, then execute as needed. Do not inline the entire script content unless debugging is required.”

This keeps the agent’s context focused while still giving access to helpers.

How Agents Actually Use Skills (Step-by-Step Flow)

1. Discovery
   Conversation starts → agent sees flat list:

   • code-review: “Reviews code changes for bugs, style…”
   • pytest-generator: “Generates pytest unit tests following Google Python style…”

2. Relevance Check
   Your prompt arrives → agent matches task keywords/semantics against descriptions.

3. Activation & Execution
   If relevant → agent loads full SKILL.md → treats instructions as high-priority context → follows them step-by-step.

You can force activation by name (“use the code-review skill on this diff”), but the real magic happens when it decides autonomously.

Best Practices That Actually Work

• Single responsibility: One skill = one clear purpose. Prefer five focused skills over one giant “do-everything” file.
• Description is make-or-break: Write for an AI reader in third person with strong trigger keywords.
  Good: “Generates REST API endpoint handlers in FastAPI following our internal security & logging conventions.”
  Bad: “Help write APIs”
• Use decision trees: For conditional logic, add explicit “If … then … else …” sections.
• Scripts as black boxes: Encourage the agent to run --help or read small parts rather than dumping entire source files into context.
• Iterate in public: After using a skill 5–10 times, add common failure patterns or clarifications back into the markdown. Skills get better the more you use them.

Frequently Asked Questions

Do I have to tell the agent to use a skill every time?
No — if the description is well-written and the task matches, the agent activates it automatically. Explicit mention (“use pytest-generator”) is only needed when you want to force it.

Can skills conflict with each other?
Rarely, but possible. If two descriptions both look relevant, the agent ranks by fit or blends them. Make descriptions very specific to reduce overlap.

Can I share skills with my team?
Yes — commit .agent/skills/ to git so everyone on the project gets the same behavior. Global skills stay personal unless you distribute the folder manually.

Are binary files or huge models allowed?
Not recommended. Large files hurt context efficiency. Stick to text, small scripts, templates, and examples.

Does this work across different LLM providers in Antigravity?
Yes — the skill system is platform-level, independent of whether you’re using Gemini, Claude, or other supported models.

Why This Matters for Real Development Work

Most AI coding tools still live in “chat mode”: you explain the same rules again and again. Agent Skills turn those repeated explanations into persistent, discoverable knowledge that travels with your project (or with you globally).

The result?

• Fewer inconsistent outputs
• Faster onboarding for new team members (they inherit the skills)
• Higher adherence to standards without nagging
• Agents that feel like they “remember” your style

If you’re already using Google Antigravity (or planning to), try this today:

1. Open any workspace
2. Create .agent/skills/my-first-skill/SKILL.md
3. Paste one piece of repeated advice you give your agent every week
4. Start a new chat and do a related task — watch how differently (and better) it behaves

Small investment, surprisingly large return.

Happy building — and enjoy how much less repetitive your agent conversations become.

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