Google Antigravity vs Cursor Rules: How to Sync Guidelines Across Agents

Google Antigravity and Cursor use fundamentally different architectures for developer guidelines: Cursor relies on editor-bound rules (.cursor/rules/*.mdc) activated via file glob matching, while Google Antigravity uses hierarchical guidelines and lazy-loaded skills (.agents/rules/, AGENTS.md, and SKILL.md) integrated into autonomous planning loops. Synchronizing guidelines across both agents prevents instruction drift without bloating context budgets.

What Is the Difference Between Google Antigravity and Cursor Rules?

Google Antigravity rules and Cursor Rules differ primarily in their execution model, triggering triggers, and runtime environments. Cursor Rules guide an interactive coding assistant embedded inside a code editor, evaluating rules based on the open file path and editor tabs. Google Antigravity rules govern autonomous, multi-step agent planning sessions running across workspaces, subagents, and tool pipelines.

As modern software engineering stacks evolve, developers rarely rely on a single AI assistant. A typical day involves writing front-end React components using Cursor's interactive Composer, followed by launching an autonomous Google Antigravity agent in the terminal to refactor background workers, execute database schema migrations, and audit security vulnerabilities.

As stated in the official Cursor Documentation: “Rules allow you to provide custom instructions to Cursor that are automatically included in your AI requests.” In Cursor, these rules are anchored to the local IDE window.

Meanwhile, the Google DeepMind Gemini and Antigravity ecosystem structures agent behavior around autonomous planners, tool boundaries, and multi-agent coordination. Understanding the mechanical differences between these two systems ensures your engineering standards are respected by both tools without wasting token capacity.

Architecture Breakdown: How Google Antigravity Rules Differ from Cursor Rules

Google Antigravity and Cursor enforce different file storage locations, frontmatter schemas, and scoping hierarchies. Cursor organizes guidelines into individual Markdown Component files under the .cursor/rules/ directory (introduced in Cursor v0.40+ to supersede monolithic .cursorrules files; if your rules fail to trigger or conflict with legacy files, see our diagnostic guide on fixing Cursor rules not working). Antigravity resolves guidelines through a multi-tiered hierarchy including AGENTS.md in the repository root, dedicated rule files under .agents/rules/, and modular Google Antigravity skills.

Consider how a strict API error-handling convention is declared in Cursor versus Google Antigravity.

1. Cursor Rule Format (.cursor/rules/api-conventions.mdc)

In Cursor, a rule file uses YAML frontmatter with explicit glob patterns to control when the instruction injects into the prompt context:

---
description: Enforce structured RFC-7807 error responses in API routes
globs: "src/app/api/**/*.ts"
alwaysApply: false
---

# API Error Handling Conventions

When generating or modifying API route handlers:
1. Never throw unhandled Error objects; return an ApiResponse<T> envelope.
2. Use RFC-7807 problem details with a valid `type`, `title`, and `status`.
3. Log internal error stack traces using logger.error() before sanitizing the payload.
4. Validate incoming request parameters with Zod schemas defined in @/lib/schemas.

2. Google Antigravity Guideline Format (.agents/rules/api-conventions.md)

In Google Antigravity, rules are structured to guide autonomous planners during tool execution, subagent spawning, and verification passes:

# Rule: API Error Handling & Validation Standards
Scope: workspace/backend
Applies-To: planner, code-modification, test-runner

## Enforcement Guidelines
- All API handlers under `src/app/api/` must return RFC-7807 structured errors.
- Never write raw `throw new Error()` statements in endpoint handlers.
- When validating payloads, instantiate schemas from `@/lib/schemas` using `.safeParse()`.
- Prior to executing git commits, invoke the test subagent to run `npm run test:api`.

## Verification Verification Criteria
- Execute `npm run lint` to verify no unhandled Promise rejections exist.
- Ensure all error logs redact bearer tokens and customer PII.

Cursor evaluates rule triggering at file-focus and tab-selection time, while Google Antigravity evaluates rule applicability dynamically during planner task decomposition. Notice that while Cursor targets specific file paths via glob matching, Google Antigravity targets agent roles, tool permissions, and verification criteria.

Context Economics: How Do Rule Triggers Affect Token Budgets?

Rule triggering mechanics directly determine context token consumption, session latency, and model reasoning quality. Cursor rules configured with alwaysApply: true inject their entire contents into every prompt turn, rapidly exhausting token limits. In contrast, Google Antigravity indexes lightweight metadata at startup and dynamically pulls full instructions into memory only when relevant tasks are triggered.

To understand the economic cost of instruction design, analyze what happens across a standard 10-turn coding session:

• Cursor Always-On Rules: If a team defines 5 project rules totaling 2,500 words (~3,500 tokens) with alwaysApply: true, Cursor injects 3,500 tokens of static guidance on turn 1, turn 2, and every subsequent turn. Across a 10-turn session, that single set of rules consumes 35,000 tokens of cumulative context. This static load leaves less room for source code context, slows down model inference, and increases billing costs.
• Cursor Glob-Filtered Rules: When rules use specific globs (such as globs: "src/components/**/*.tsx"), Cursor injects the 400-token rule only when the developer views or modifies matching files. This reduces unnecessary token waste during non-UI tasks by over 80%.
• Google Antigravity Lazy Loading: Antigravity scans rule and skill manifests during initialization, indexing only 35 to 50 tokens of frontmatter metadata per guideline. A library of 20 guidelines consumes fewer than 1,000 tokens of baseline overhead. The planner pulls the full 2,000-word guideline into context only when it delegates an API refactoring subagent or initiates a test execution loop, achieving a 92% reduction in idle token consumption.

Always-on Cursor Rules inject their entire markdown body into every model interaction, whereas Google Antigravity uses lazy-loaded rule metadata to keep baseline context overhead under 150 tokens.

Direct Comparison: Google Antigravity Rules vs Cursor Rules at a Glance

Evaluating Google Antigravity and Cursor side-by-side demonstrates how their design philosophies diverge across scoping, token budgets, and runtime execution.

The comparison table below details the technical dimensions of both systems alongside standard AGENTS.md and CLAUDE.md instruction formats:

How Do You Convert Cursor Rules to Google Antigravity Guidelines?

Converting Cursor Rules into Google Antigravity guidelines requires stripping editor-specific glob frontmatter and translating passive file rules into actionable agent constraints. Because Google Antigravity agents execute multi-step workflows, rules must define clear execution preconditions, allowable tool actions, and verification commands rather than simple syntax reminders.

Follow this four-step migration process to convert any existing Cursor rule into an Antigravity guideline:

1. Extract the Core Constraint: Separate the fundamental engineering standard (e.g., “all database queries must use Prisma transactions”) from the editor-specific glob pattern (globs: "src/db/**/*.ts").
2. Define Actionable Tool Policies: In Cursor, a rule passively warns the AI. In Antigravity, rules can explicitly restrict or encourage specific tool calls, such as directing the agent to use run_command to execute schema validations before applying file edits.
3. Add Verification and Test Criteria: Specify the exact shell commands the Antigravity agent must execute to verify compliance (such as npm run typecheck or npm run test:db) before marking a task complete.
4. Choose the Placement Tier: Place repository-wide architectural rules in AGENTS.md at the root of the project. Place domain-specific rules (like migrations or security policies) in .agents/rules/ or as modular skills in skills/.

The Sprawl Moment: When Dual-Agent Guidelines Collide

A senior backend developer sets up a comprehensive suite of architectural rules inside Cursor on their main MacBook. Over four weeks, they author six detailed .cursor/rules/*.mdc files enforcing strict Zod payload validations, Prisma multi-tenant query isolations, and immutable event-logging conventions. In Cursor, the interactive assistant follows every rule flawlessly. On Thursday afternoon, the developer needs to perform an automated repository-wide upgrade of twelve background worker services and launches an autonomous Google Antigravity agent in the terminal. Because the guidelines are buried inside .cursor/rules/, Antigravity has no knowledge of them. The autonomous agent generates raw SQL queries, ignores Prisma transactions, and commits code that completely bypasses the Zod validation layer. The developer discovers the discrepancy only after tests fail across three staging environments, spending four hours manually porting the Cursor rules into Antigravity configurations.

Prompttly is a skill manager for AI agents — one library for your skills and prompts that syncs into Claude Code, Codex, ChatGPT, and Claude and is one hotkey away on your Mac, so your setup follows you across every machine, repo, and tool.

Can You Maintain a Single Source of Truth Across Antigravity and Cursor?

Yes, you can maintain a single source of truth across Google Antigravity and Cursor by managing canonical guidelines in an agent-neutral library and syncing them automatically. Rather than maintaining parallel directories of .cursor/rules/*.mdc, AGENTS.md, and Antigravity rule manifests by hand, modern engineering teams centralize their guidelines in a unified skill manager.

Cursor Rules are bound to the Cursor editor runtime and evaluate file patterns via glob matching, whereas Google Antigravity Rules govern autonomous agent execution trees across multi-workspace terminals and specialized subagents. Because neither tool parses the other's configuration directory natively, a bridge is necessary.

Prompttly solves cross-agent rule drift by providing:

• Two-Way Filesystem Synchronization: Keep your canonical rules in Prompttly's cloud library. Prompttly writes native .cursor/rules/*.mdc files into Cursor projects and synchronizes AGENTS.md and SKILL.md folders into Google Antigravity and Claude Code automatically.
• Sub-200ms Mac Hotkey Palette: Press a global keyboard shortcut from anywhere on your Mac to instantly search your entire library of prompts, rules, and skills, inserting approved directives directly into Cursor Composer, Antigravity chat, or web interfaces.
• Unified Version History: Track revisions to your engineering guidelines across machines and team members without relying on disconnected local dotfiles or scattered git branches.
• Direct MCP Connectivity: Expose your centralized prompt and skill library to Google Antigravity, Claude Code, and Codex over the Model Context Protocol (MCP) with full read and write capabilities. Learn how this works in our guide on building a multi-machine AI skill sync pipeline using MCP.

A unified agent guideline repository requires storing canonical rules in an agent-neutral schema and compiling them down to .cursor/rules/*.mdc and Antigravity workspace configurations automatically.

When NOT to Use a Dedicated Skill Manager

A dedicated skill manager like Prompttly is not necessary for every developer or development team. Understanding when native editor configurations are sufficient prevents unnecessary workflow complexity.

You do not need an external skill manager if:

• You use exactly one AI tool on one machine: If your entire development workflow happens exclusively inside Cursor on a single laptop, local .cursor/rules/*.mdc files committed to your git repository provide everything you need.
• Your rule library consists of fewer than three simple guidelines: A single 20-line AGENTS.md file in your repository root is completely adequate to tell both Cursor and Antigravity what package manager and linter your project uses.
• You never switch between interactive IDEs and autonomous terminal agents: If you never run autonomous agents like Google Antigravity, Claude Code CLI, or OpenAI Codex, you will not experience cross-agent guideline drift.

However, the moment you work across two or more tools (such as Cursor and Google Antigravity), operate across multiple machines, or share guidelines with a team of engineers, manual copying fails. That is the threshold where a centralized skill manager becomes essential.

Frequently Asked Questions About Google Antigravity and Cursor Rules

Next Steps for Optimizing Your Multi-Agent Workflow

Structuring guidelines for both interactive IDE assistants and autonomous agents is the foundation of a modern AI-assisted engineering stack. Explore our related architectural resources to streamline your agent infrastructure:

• Google Antigravity Agent Skills: Architecture, Formats, and Portability — A deep dive into Antigravity SKILL.md schemas, subagent hierarchies, and filesystem resolution.
• Claude Skills vs Cursor Rules, CLAUDE.md, and AGENTS.md — How to balance persistent repo context with modular, portable agent skills.
• Cursor Rules (.cursorrules) vs Windsurf Cascade Rules: Portability Guide — Comparing modular MDC rules with Windsurf's centralized memory architecture.
• Fixing Broken AI Agent Instructions Across Repositories and Worktrees — How to manage rule sprawl across polyrepos, microservices, and git worktrees.
• Free Custom Instructions Generator — Generate clean, structured custom instruction templates formatted for AI agents and chat interfaces.