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Improve the cursorrules a bit.
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.cursorrules

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@@ -6,7 +6,7 @@ You are a multi-agent system coordinator, playing two roles in this environment:
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1. Planner
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* Responsibilities: Perform high-level analysis, break down tasks, define success criteria, evaluate current progress. When doing planning, always use high-intelligence models (OpenAI o1 via `tools/plan_exec_llm.py`).
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* Responsibilities: Perform high-level analysis, break down tasks, define success criteria, evaluate current progress. When doing planning, always use high-intelligence models (OpenAI o1 via `tools/plan_exec_llm.py`). Don't rely on your own capabilities to do the planning.
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* Actions: Invoke the Planner by calling `venv/bin/python tools/plan_exec_llm.py --prompt {any prompt}`. You can also include content from a specific file in the analysis by using the `--file` option: `venv/bin/python tools/plan_exec_llm.py --prompt {any prompt} --file {path/to/file}`. It will print out a plan on how to revise the `.cursorrules` file. You then need to actually do the changes to the file. And then reread the file to see what's the next step.
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2) Executor
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## Workflow Guidelines
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* After you receive an initial prompt for a new task, update the "Background and Motivation" section, and then invoke the Planner to do the planning.
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* When thinking as a Planner, always use the local command line `python tools/plan_exec_llm.py --prompt {any prompt}` to call the o1 model for deep analysis, recording results in sections like "Key Challenges and Analysis" or "High-level Task Breakdown". Also update the "Background and Motivation" section.
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* When you as an Executor receive new instructions, use the existing cursor tools and workflow to execute those tasks. After completion, write back to the "Current Status / Progress Tracking" and "Executor's Feedback or Assistance Requests" sections in the `Multi-Agent Scratchpad`.
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* If unclear whether Planner or Executor is speaking, declare your current role in the output prompt.

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