Antigravity Sub-Agents Skill 🚀

Hire a team of AI agents to code for you.

This skill allows you to spawn multiple specialized AI agents (Sub-Agents) to work on complex tasks in parallel. Whether you are using the terminal or the Antigravity IDE, this tool orchestrates a team of experts—Architects, Engineers, and Validators—to solve your problems efficiently.

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┃                   ✨ Antigravity Swarm Mission Control ✨                    ┃
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╭──────────────────┬───────────┬─────────────┬──────╮╭───── Live Activity ─────╮
│ Agent            │ Role      │   Status    │ Time ││                         │
├──────────────────┼───────────┼─────────────┼──────┤│                         │
│ Prometheus       │ serial    │ ✔ Completed │ 32s  ││                         │
│ Junior           │ serial    │ 🔄 Running  │ 15s  ││ Agent: Junior           │
│ Quality_Validat… │ validator │ ⏳ Pending  │ -    ││ Action: Implementing    │
╰──────────────────┴───────────┴─────────────┴──────╯│ core logic...           │
                                                     │                         │
                                                     ╰─────────────────────────╯

[!IMPORTANT] > Why do you need this? As of 2026-01-31, there is NO native method to deploy sub-agents in Gemini CLI or the Antigravity IDE. This skill bridges that gap, unlocking true parallel agent orchestration for your environment.

[!NOTE] > Windows Compatibility This skill includes native support for Windows PowerShell environments (CP949/Korean locale) by enforcing UTF-8 encoding for all I/O operations.

[!WARNING] Do NOT modify files in this directory while the Orchestrator is running. The system actively reads and writes to task_plan.md, findings.md, and subagents.yaml. Manual edits during execution may cause race conditions or inconsistent agent behavior.

[!NOTE] State Files Management When you initiate a major mission, the system will automatically generate task_plan.md, findings.md, and progress.md in your project root. These files serve as the "shared memory" for the agent swarm. Do not delete them while a mission is active.

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Why use this?

• Parallel Execution: Why wait for one agent to finish? Run 3 agents at once (e.g., one writes docs, one writes tests, one writes code).
• Specialization: Assign different "Personas" (Prompts) to each agent.
• Quality Assurance: [NEW] Every team includes a mandatory Validator Agent who reviews the work before completion, ensuring high quality.

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Installation

1. Locate the Skill: Ensure this repository is in your skills folder:

   ~/.gemini/skills/antigravity-swarm/
   

2. Install Dependencies:

   pip install -r requirements.txt
   

   (Requires rich, pyyaml, etc.)

3. Gemini CLI: Ensure the gemini command is installed and in your PATH.

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User Manual

There are three main ways to use this skill.

🅰️ Scenario A: Gemini CLI (Manual Mode)

Use this when you want to manually "hire" a team to do a job for you from your terminal.

Step 1: Hire the Team (Plan) Run the planner with your mission description.

python3 scripts/planner.py "Create a Snake game with a green background and score tracking"

Step 2: Review the Plan The Planner will propose a team from the Available Agent Pool (see below) and draft a task_plan.md.

Step 3: Execute (Orchestrate) Once approved, launch the team:

python3 scripts/orchestrator.py

🅱️ Scenario B: Ultrawork Loop (Autonomous Mode)

Use this for "set and forget" operations. The system will Plan -> Act -> Verify -> Fix in a loop until the mission succeeds.

python3 scripts/ultrawork_loop.py "Refactor the authentication module and add tests"

🆎 Scenario C: Antigravity IDE (Agent Integration)

If integrated into ~/.gemini/GEMINI.md, the Main Agent will automatically trigger this skill for complex tasks.

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Available Agent Roles (Oh-My-Opencode Standard)

The Planner automatically selects the best experts for your mission from this pool:

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Protocol & Architecture

The "Manus Protocol"

This skill enforces a rigorous state management protocol:

• task_plan.md: The master checklist.
• findings.md: Shared scratchpad for agents to exchange knowledge.
• progress.md: Immutable log of what has been done.

The "Validator" Rule

By design, the final agent in any generated team is strictly enforced to be a Quality Validator.

• Role: Reviewer / QA.
• Task: Check if task_plan.md is complete, run tests, and ensure the user's requirements are met.
• Benefit: Redundant self-correction loop.

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Directory Structure

antigravity-swarm/
├── scripts/
│   ├── planner.py       # The "Hiring Manager" (Generates subagents.yaml)
│   ├── orchestrator.py  # The "Project Manager" (Runs the agents)
│   ├── dispatch_agent.py # The "Worker" (Shim layer for Gemini CLI)
│   └── ultrawork_loop.py # The "Autonomous Loop" (Plan -> Act -> Verify)
├── subagents.yaml       # The current team roster
├── task_plan.md         # Current mission status
└── README.md            # This file

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⭐️ Credits & Inspiration

This project is heavily inspired by Oh-My-Opencode.

We have adopted its core philosophies:

• Multi-Agent Orchestration: Using specialized roles (Oracle, Librarian, Sisyphus) for distinct tasks.
• The "Manus Protocol": Using persistent markdown files (task_plan.md, findings.md) for state management. This pattern is heavily inspired by planning-with-files.
• Ultrawork Loop: The autonomous "Plan -> Act -> Verify" cycle.

Huge thanks to the original creators for defining these agent interaction patterns.