Antigravity Subagents Skill
This skill allows you to dispatch autonomous sub-agents to perform tasks. It features a Manager layer (planner.py) that can automatically design a team of agents for a complex mission, and an Orchestrator (orchestrator.py) to run them visually. Both scripts include a Plan Mode (confirmation step) by default to prevent accidental usage limits consumption.
[!WARNING] Do NOT modify files in this directory while the Orchestrator is running. The system actively reads and writes totask_plan.md,findings.md, andsubagents.yaml. Manual edits during execution may cause race conditions or inconsistent agent behavior.
🛠 Tools
dispatch_subagent
Runs a sub-agent with a specific task.
Usage: Use this when you have a parallelizable task or need to offload a specific job (e.g., "Write a test file", "Analyze this directory").
Arguments:
task: A clear, self-contained description of what the sub-agent should do.
Implementation Details: The sub-agent is powered by the gemini CLI. A Python wrapper intercepts specific output patterns to perform file system operations and command executions.
Syntax used by Sub-Agent (Handled Automatically):
<<WRITE_FILE path="...">>...<<END_WRITE>><<RUN_COMMAND>>...<<END_COMMAND>>
run_mission (Dynamic Orchestration)
Analyzes a high-level goal, hires a custom team of sub-agents, and creates a configuration for them.
Usage: Use this for complex, multi-step projects where you don't want to manually define every sub-agent.
Arguments:
mission: A description of the overall project (e.g., "Create a Snake game in Python").
How it works:
- Calls
scripts/planner.pyto generatesubagents.yaml. (Will prompt for confirmation unless--yesis used). - (Optional) You can then run
scripts/orchestrator.pyto execute the team. (Will prompt for confirmation unless--yesis used).
Usage Modes
Mode 1: CLI User (Terminal Visualization)
Run the Python orchestrator in your terminal to see a TUI.
python scripts/orchestrator.pyMode 2: IDE Agent (Chat Visualization)
As an Agent, you act as the Orchestrator.
- Spawn: Use
run_commandto launch sub-agents in the background. Use--format jsonfor logs.python scripts/dispatch_agent.py "Task A" --log-file logs/agent_a.json --format json & python scripts/dispatch_agent.py "Task B" --log-file logs/agent_b.json --format json & - Monitor: Poll the JSON log files to check for
{"type": "status", "content": "completed"}. - Visualize: Render a Markdown dashboard in your chat response to the user.
🚀 Examples
1. Manual Dispatch (Single Agent)
run_command("python3 scripts/dispatch_agent.py 'Create a file named hello.py that prints Hello World'")2. Auto-Hire a Team (Mission Mode)
# 1. Generate the team
run_command("python3 scripts/planner.py 'Create a fully functional Todo List app in HTML/JS'")
# 2. Run the team
run_command("python3 scripts/orchestrator.py")[!WARNING] You must usegemini-3-proorgemini-3-flash. Deprecated or older models may not support the file shim protocol correctly.
❓ FAQ & Philosophy
Why Sub-agents?
- Context Isolation: Prevents "Context Contamination." A UI Specialist doesn't need to see specific Database Migration code. Separation ensures higher accuracy.
- Scalability: While loop-based agents process sequentially, Sub-agents are architected to run in parallel threads.
- Fault Tolerance: If one sub-agent fails (e.g., Syntax Error), it doesn't crash the entire mission; the Orchestrator can retry just that agent.
Is this truly parallel?
Yes. orchestrator.py uses Python's threading.Thread to spawn separate OS processes for each agent. *Note: You may perceive sequential behavior if the underlying gemini CLI tool enforces a global lock or if you hit API Rate Limits.*
Planning with Files (Manus Protocol)
This skill adheres to the "Manus" state management philosophy. All agents operate on a shared set of "Memory Files" in the root of the workspace:
task_plan.md: The Source of Truth for the mission checklist.findings.md: A shared scratchpad for discoveries and research.progress.md: A log of completed steps and current status.