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- google antigravity agent scaffolder
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google antigravity agent scaffolder
Architect and scaffold modular multi-agent systems with structured handoffs for Google Antigravity IDE.
$15
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
- Register the
AUDITOR_SCHEMAin your Antigravity Agent Configuration panel. - Implement the
execute_agentwrapper to interface with your specific Antigravity runtime environment. - Add a
Loggertool 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
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.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
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