Antigravity CLI Implementation

Antigravity CLI (Gemini) optimization strategies including GEMINI.md separation, Implementation Plan artifacts, and hook-based guardrails

Token Optimization Guide / Antigravity CLI Implementation

Antigravity CLI Token Optimization

Layer 1 β€” Platform Mechanics

Keep GEMINI.md to Antigravity-only overrides; put shared, cross-tool rules in ~/.gemini/AGENTS.md to sidestep the Gemini CLI path conflict. Skills in ~/.gemini/config/skills/ follow the same load-on-match principle as Claude/Codex. Route lightweight or parallel subagents (e.g. a browser-testing subagent) to a faster model tier via their YAML model field, reserving your primary tier for the main coding loop.

Key Strategies:

  • File Separation: Separate Antigravity-specific from shared rules
    • GEMINI.md: Antigravity-only overrides (keep thin)
    • ~/.gemini/AGENTS.md: Shared, cross-tool rules
    • Avoids Gemini CLI path conflict
  • Skills Progressive Disclosure: Skills in ~/.gemini/config/skills/
    • Load-on-match principle like Claude/Codex
    • Reduce always-loaded token overhead
  • Tiered Subagent Routing: Route subagents by workload type
    • Lightweight/parallel subagents: Faster model tier
    • Main coding loop: Primary model tier
    • Configure via YAML model field in agent definitions

Layer 2 β€” Agent Operation

Antigravity’s Implementation Plan artifact is a built-in plan gate β€” explicitly ask for a plan first on non-trivial tasks so exploration and editing don’t happen uncontrolled in the same pass. Use PreToolUse-style hooks to hard-block expensive or risky operations (e.g. a forced push) rather than relying on the model to self-police.

Operational Best Practices:

  • Implementation Plan Artifact: Built-in plan gate
    • Explicitly request plan first on non-trivial tasks
    • Separate exploration from editing
    • Prevent uncontrolled mixed-mode operations
  • Hook-Based Guardrails: Use PreToolUse-style hooks
    • Hard-block expensive operations (e.g., force push)
    • Don’t rely on model self-policing
    • Enforce operational constraints at tool level
  • Session Management: Clear separation between planning and execution phases

Layer 3 β€” Codebase Architecture

Duplicate the Conventions section into AGENTS.md (shared) or GEMINI.md only if Antigravity-specific phrasing is genuinely needed.

Architecture Rules:

  • Shared Conventions: Use AGENTS.md for cross-tool compatibility
  • Tool-Specific Overrides: Use GEMINI.md only when Antigravity-specific phrasing is needed
  • Avoid Duplication: Don’t duplicate content between files unnecessarily

Antigravity CLI-Specific Features

Implementation Plan Artifact

Antigravity’s native Implementation Plan provides structured planning:

  • Built-in plan gate separate from execution
  • Structured approach to complex tasks
  • Natural separation of exploration and implementation

YAML Agent Definitions

Subagent definitions in .antigravity/agents/ or ~/.config/antigravity/agents/:

  • YAML-based agent configuration
  • Per-agent model selection
  • Support for complex agent orchestration

Hook System

Antigravity’s hook system allows operational control:

  • PreToolUse hooks for blocking operations
  • PostToolUse hooks for logging and auditing
  • Hard enforcement of operational constraints

Skills Integration

Skills in ~/.gemini/config/skills/:

  • Follow same progressive disclosure pattern as other tools
  • Load-on-demand based on task matching
  • Reduce always-loaded context overhead

Path Conflict Resolution

Critical note for users with both Gemini CLI and Antigravity CLI:

  • Both write to ~/.gemini/GEMINI.md
  • Can silently pollute context with wrong tool’s rules
  • Deliberate separation required for clean operation

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Future References

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Spec-Driven Development

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Awesome Copilot

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Promptfoo

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Pattern Sources

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Refactoring.Guru

Classic GoF design patterns, code smells catalog, and refactoring techniques (https://refactoring.guru).

Enterprise Integration Patterns

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Microservices.io

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Agent Catalog Patterns

Patterns for agentic systems from agentpatternscatalog.org (CC BY 4.0).

OWASP Foundation

Security patterns from OWASP Top 10 for Web Applications, LLM Applications, and Agentic Applications (CC BY-SA 4.0).

Industry Research

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AI Agent Patterns

Spec-driven development patterns from Claude, Gemini, OpenAI, and GitHub Copilot on github/spec-kit and OpenSpec.

Data Engineering Leaders

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MLOps Best Practices

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Streaming & Analytics

Real-time patterns from Confluent/Kafka, Apache projects, and serverless analytics platforms.

Academic Papers

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5-Day AI Agents Course

Intensive Vibe Coding Course With Google by Brenda Flynn et al. (2026) on Kaggle.

The Agent Loop

Foundational Agent Definition (Perceive + Act):
Russell, S. J., & Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice Hall. (Current edition: 4th Ed., Pearson, 2020)

Modern Iterative LLM Agent Loop:
Yao, S., Zhao, J., Yu, D., et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv:2210.03629.

Historical Context:
Incorporating AIMA's perceive/act model, Classical robotics' Sense-Plan-Act loop (Brooks, 1986), and ReAct's Thought→Action→Observation cycle.