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    google antigravity agent scaffolder

    1

    Architect and scaffold modular multi-agent systems with structured handoffs for Google Antigravity IDE.

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    google antigravity agent scaffolder

    google antigravity agent scaffolder

    Example session with this skill installed

    Scaffold a two-agent system for a code review workflow. Agent A audits for security, then hands off a structured summary to Agent B who suggests specific fixes.

    • Read your context and instructions
    • Compiled the google antigravity agent

    I have architected a two-agent audit-and-fix pipeline. This setup uses a structured ReviewState object to ensure Agent B receives actionable security findings without the noise of the raw audit logs.

    Antigravity Multi-Agent Code Review Scaffolding

    import json
    
    # 1. State Definition
    class ReviewState:
        def __init__(self):
            self.vulnerabilities = [] # List of {severity, loc, description}
            self.code_context = ""
            self.audit_complete = False
    
    # 2. Agent A: Security Auditor
    # Persona: Expert AppSec Engineer
    # Output Schema: Strict JSON array of vulnerability objects
    AUDITOR_SCHEMA = {
        "type": "object",
        "properties": {
            "findings": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "severity": {"type": "string", "enum": ["LOW", "MEDIUM", "HIGH", "CRITICAL"]},
                        "line": {"type": "integer"},
                        "issue": {"type": "string"}
                    },
                    "required": ["severity", "issue"]
                }
            }
        }
    }
    
    # 3. Handoff Logic
    def coordinate_review(source_code):
        state = ReviewState()
        state.code_context = source_code
        
        # Trigger Auditor
        raw_audit = execute_agent("Security_Auditor", {"code": source_code, "schema": AUDITOR_SCHEMA})
        state.vulnerabilities = json.loads(raw_audit).get("findings", [])
        state.audit_complete = True
        
        # Structured Handoff to Agent B (Fixer)
        if state.vulnerabilities:
            handoff_payload = {
                "target_code": state.code_context,
                "issues_to_fix": state.vulnerabilities,
                "instruction": "Provide diffs for each finding."
            }
            return execute_agent("Remediation_Expert", handoff_payload)
        
        return "No vulnerabilities found."
    

    Next steps

    1. Register the AUDITOR_SCHEMA in your Antigravity Agent Configuration panel.
    2. Implement the execute_agent wrapper to interface with your specific Antigravity runtime environment.
    3. Add a Logger tool to Agent A to persist raw findings to your database before the handoff.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Design modular agent personas with strict JSON output schemas.Implement structured state-passing to replace messy conversation histories.Create robust tool definitions with built-in error handling patterns.Scaffold complex multi-agent workflows for Google Antigravity IDE.

    About this skill

    The problem

    Building autonomous agents in Google Antigravity often leads to monolithic, brittle prompts that crash during tool execution or lose context during handoffs. Developers struggle with maintaining deterministic state when passing data between multiple specialized agents.

    What it does

    • Generates three-layer agent architectures separating personas, tool schemas, and memory states.
    • Produces strict JSON output schemas to ensure predictable parsing within the Antigravity IDE.
    • Defines robust tool-calling interfaces with explicit parameter typing and error-handling patterns.
    • Architects state-passing mechanisms that prevent context window bloat by using structured state objects.

    Frameworks & tools

    Google Antigravity IDE and Python-based AI agent frameworks.

    Why this beats prompting it yourself

    Standard prompts often result in messy conversation histories that confuse agents during handoffs. This skill enforces a professional orchestration pattern that treats agents like modular microservices, ensuring higher reliability and lower token usage through structured state management.

    Use cases

    • Scaffolding a multi-agent research pipeline with distinct specialist nodes.
    • Designing custom tool schemas for external API integrations in Antigravity.
    • Implementing persistent memory objects for long-running agentic workflows.
    • Creating supervisor agents to orchestrate sub-agent handoffs.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Security scanned

    Verified clean 6 days ago

    • Passed all security checks, Safe to install

    Listed6 days ago

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