Google Antigravity: How to Connect Rules, Skills, and Workflows for Reliable AI Agents

Google Antigravity is gradually transforming an AI assistant from an autocomplete tool into a customizable engineering agent: instead of one-off suggestions, a developer provides it with working rules, individual skills, and ready-made scenarios, then launches a repeatable QA process with a single command. In practice, this means that code review, refactoring, and test generation can be assembled into a local pipeline without external orchestrators and without constant manual oversight at every step. The key idea revolves around three entities.

Rules set basic constraints for the agent: code style, accepted practices, and technological framework of the project. Skills act as reusable knowledge packages for a specific task, such as review, deployment, or test writing. Workflows link all of this into a sequence of actions that can be invoked with a slash command.

In Google's documentation, workflows are compared to saved prompts, and rules are compared to system instructions. This layering is needed so the agent doesn't pull the entire set of instructions into context at once and connects specialized instructions only as needed.

Separately, Google makes a bet on progressive disclosure. A skill doesn't hang permanently in the model's context: first, the agent sees only a brief description of the skill, and pulls the full instructions only when the task actually matches its purpose. This reduces overload from unnecessary tools and rules, decreases latency, and helps prevent the model's behavior from becoming diffuse. The skill itself can be more than just a text note—it can be a small project folder with a SKILL.md file, additional scripts, reference materials, and assets. Skills can be stored globally for all projects or locally within a specific workspace if they are tied to the stack and processes of a single team.

The practical scenario discussed for Antigravity is quite down-to-earth: QA checking of Python code. In the workspace, folders .agents/rules and .agents/skills are created, after which a rule is added for Python with PEP 8 requirements, use of black for formatting, and restrictions on free open-source dependencies. This rule can be applied to a glob mask for all .py files so it automatically applies to any Python code. Separately, a skill is created for test generation: it sits in its own directory, contains a required SKILL.md file, and describes how the agent should write coverage for already-verified code. This approach makes the configuration modular: project standards live separately from one-off commands in chat.

On top of these blocks, a workflow is assembled with a name like qa-check. In it, the agent is sequentially tasked with opening the current Python file, finding bugs and style violations, simplifying inefficient sections if needed, then calling the test skill and finally outputting ready-made unit tests with a recommendation to run pytest in the terminal. For demonstration, an intentionally careless division function is used with poor formatting and no handling of division by zero.

After running the workflow, the agent doesn't just rewrite the code in a neater form, but also proposes a test set for normal, negative, and fractional values, as well as a separate case for division by zero with an expected error. This is an important point: Antigravity here doesn't limit itself to cosmetic refactoring but takes the task through to a verifiable result, where quality is confirmed by test logic.

This approach has two practical consequences. First, teams can assemble repeatable engineering rituals inside the IDE without a separate automation layer on top of the editor. Second, the AI agent begins to work not as a universal conversationalist but as a specialized executor with a clear zone of responsibility. For development, this means less manual switching between review, fixes, and testing, and for Google, another step toward making Antigravity compete not only with code assistants but with entire agent-based work environments.