fix(mma): Assign dedicated models per tier in execute_agent
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@@ -2,26 +2,36 @@ import argparse
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import subprocess
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import json
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def get_model_for_role(role: str) -> str:
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"""Returns the specific model to use for a given tier role."""
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if role == 'tier1-orchestrator' or role == 'tier1':
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return 'gemini-3.1-pro-preview'
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elif role == 'tier2-tech-lead' or role == 'tier2':
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return 'gemini-3.0-flash-preview'
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else:
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return 'gemini-2.5-flash-lite'
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def get_role_documents(role: str) -> list[str]:
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if role == 'tier1':
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if role == 'tier1-orchestrator' or role == 'tier1':
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return ['conductor/product.md', 'conductor/product-guidelines.md']
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elif role == 'tier2':
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elif role == 'tier2-tech-lead' or role == 'tier2':
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return ['conductor/tech-stack.md', 'conductor/workflow.md']
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elif role == 'tier3':
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elif role == 'tier3-worker' or role == 'tier3':
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return ['conductor/workflow.md']
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return []
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def execute_agent(role: str, prompt: str, docs: list[str]) -> str:
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model = get_model_for_role(role)
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command_text = f"Activate the mma-{role} skill. {prompt}"
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for doc in docs:
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command_text += f" @{doc}"
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cmd = ['gemini', '-p', command_text, '--output-format', 'json', '--model', 'gemini-2.5-flash-lite']
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cmd = ['gemini', '-p', command_text, '--output-format', 'json', '--model', model]
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try:
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process = subprocess.run(cmd, capture_output=True, text=True, shell=True)
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if not process.stdout and process.stderr:
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return f"Error: {process.stderr}"
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stdout = process.stdout
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start_index = stdout.find('{')
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if start_index != -1:
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@@ -39,7 +49,7 @@ def create_parser():
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parser = argparse.ArgumentParser(description="MMA Execution Script")
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parser.add_argument(
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"--role",
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choices=['tier1', 'tier2', 'tier3', 'tier4'],
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choices=['tier1', 'tier2', 'tier3', 'tier4', 'tier1-orchestrator', 'tier2-tech-lead', 'tier3-worker', 'tier4-qa'],
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required=True,
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help="The tier role to execute"
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)
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