The Paradox of the Expert Model: Why Google Antigravity or Claude Code Needs “Skills”

We often assume that because large language models (LLMs) like Gemini 3 are “experts” in coding and reasoning, they should be able to handle any task out of the box. If the model already knows Python, SQL, and general reasoning, why do we need to explicitly define “Skills”?

I recently dove into the documentation for Google Antigravity, and it raises a fundamental question: If the model is already an expert, then why do we need skills?

Here is a deep dive into the architecture, the philosophy, and the practical necessity of “Skills” in the age of AI agents.

1. Bridging the Gap Between “Knowing” and “Doing”

Even though the underlying model is a general-purpose expert, Skills bridge the critical gap between theoretical knowledge and your specific environment.

According to the Antigravity documentation, here is why Skills are necessary:

• Access to “Tribal Knowledge”: The model knows general coding best practices, but it doesn’t know your team’s specific deployment protocols, internal style guides, or proprietary database schemas. Skills allow you to “hard-code” this institutional memory (e.g., a check_staging_health skill) so the agent acts like a tenured employee rather than a generic freelancer.
• From Text to Action: A model is fundamentally a text completion engine. While it can write a SQL query, it cannot inherently execute it. Skills allow the agent to interface with the real world by running scripts (Python, Bash), accessing local files, or querying databases to perform multi-step workflows autonomously.
• Context Efficiency: You cannot fit every single tool and rule into the model’s context window (the “system prompt”) without causing confusion or latency. Skills use Semantic Triggering, meaning the agent only “equips” the specific instructions and tools it needs for the current task, keeping its focus sharp and memory clean.
• Standardization: Skills ensure the agent follows a strict process every time. For example, a “Code Review” skill can force the agent to always check for specific security vulnerabilities before approving a change—consistency that a raw model might miss.

2. “But Isn’t Text-to-Action Default Behavior?”

A common counter-argument is that modern agents already have a runtime. As I noted in my own exploration:

“The model is fundamentally a text completion engine—it can write a SQL query… and the runtime allows it to execute it. So it doesn’t need a Skill to run scripts.”

This is a sharp distinction, and technically correct. The Antigravity Runtime (the infrastructure) provides the capability to execute code. The “Text to Action” loop is native behavior.

However, Skills serve a different, critical set of purposes beyond just execution:

Discovery (The “Menu”)

Even though the agent can run a script, it has no way of knowing that a specific script exists in your repository unless you tell it.

• Without Skills: The model is blind to your codebase. It doesn’t know you have a file called deploy_utils.py.
• With Skills: You explicitly expose these tools. The Skill definition acts as a menu entry: “I have a tool called Deploy to Staging. If you want to use it, execute this specific command.”

Reliability (Pre-written vs. Generated)

This is the biggest difference between a “native runner” and a “Skill.”

• Native Behavior: The model must hallucinate/generate the code to perform a task. If you ask it to “query the database,” it has to write raw SQL from scratch. It might hallucinate a table name or write a syntax error.
• Skill Behavior: The logic is hard-coded by you. The model doesn’t write the code; it just calls the function you wrote. This guarantees that complex tasks happen exactly the same way every time.

The Guardrail

Native script execution is powerful but dangerous. If the model tries to solve a problem by generating rm -rf /, the native runner might just execute it. A Skill restricts the model to a specific set of allowed actions.

The Analogy: Think of Native Execution as a generic Terminal window. Think of Skills as the installed CLI Commands (like git or docker). The terminal can run anything, but without the installed commands, it doesn’t know how to do the specific work you need.

3. Are Skills Just Fancy Prompting?

The short answer: Yes, but they are dynamic, conditional prompting.

If you put every single rule and workflow into the main system prompt, you would crash the model’s performance due to Context Saturation.

• Confusion: The model gets distracted. If you ask “fix this typo,” it might hallucinate that it needs to run a database migration because that text is in its view.
• Cost/Latency: You are paying for thousands of useless tokens for every simple request.

Skills are distinct because they are “swapped in” on demand. The engine looks at your request (“Deploy this”), searches your Skills folder, and only then injects the Deploy Skill prompt. When the task is done, it “forgets” that skill.

This shifts the interaction from Probabilistic to Deterministic.

• Prompting: You tell the model “Run the tests.” It guesses the command (npm test? yarn test?).
• Skills: You wrap a specific script inside a Skill. Now, the model is forced to execute the exact file you provided.

4. The Meta-Skill: Should an Agent Build Itself?

Do you need a Skill that makes the agent better at creating Skills?

Strictly speaking, no. But practically speaking, it is one of the best first Skills you can build.

The “Blank Page” Problem

If you ask the raw model to “Create a Skill,” it might forget the specific YAML schema or save the file to the wrong directory.

The “Skill-Builder” Enforces Standards

A “Skill-Creator” Skill acts as a compiler/linter for your agent’s capabilities. Instead of the agent guessing the format, you provide a tool (e.g., scaffold_new_skill) that:

1. Generates the Folder Structure: Creates the correct directory.
2. Writes the Boilerplate: Populates the skill.yaml with the correct schema.
3. Registers it: Reloads the runtime so the new skill is immediately available.

This turns the model from a junior developer (who writes code but breaks the build) into a senior architect (who uses verified templates).

5. Claude Code vs. Google Antigravity

Finally, how does this compare to other tools like Claude Code?

They are remarkably similar—practically siblings. Both have converged on the Agent Skills Standard:

• Definition: Folder containing SKILL.md.
• Logic: Natural Language instructions.
• Execution: Scripts in ./scripts/ (Python/Bash).

The difference is in the Philosophy:

• Claude Code: The “Super-Terminal” approach. Skills feel like custom slash commands in a CLI.
• Google Antigravity: The “Tribal Knowledge” approach. Skills feel like institutional memory within an IDE.

Because the core instruction file is just Markdown, you can often copy-paste a skill from .claude/skills to .agent/skills with minimal changes.

Final Thoughts

The model provides the intelligence, but Skills provide the infrastructure. They are the difference between an AI that can “write code” and an AI that can actually “do the job.”