dinos
57 lines
3.0 KiB
Python
57 lines
3.0 KiB
Python
"""Reflection tool for strategic decision-making."""
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from langchain_core.tools import tool
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@tool(parse_docstring=True)
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def think_tool(reflection: str) -> str:
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"""Tool for structured reflection and strategic decision-making.
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Use this tool to pause and reason carefully at any decision point — not just
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after searches, but before, during, and after any significant step. This creates
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a deliberate checkpoint for quality thinking.
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When to use:
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- Before starting work: What do I know? What skills and prior knowledge are available?
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- After obtaining results: What did I learn? Does this change the approach?
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- When choosing between options: What are the trade-offs? Which path is strongest?
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- When stuck or failing: What went wrong? Is there a proven strategy to apply?
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- Before concluding: Is the evidence sufficient? What does the next phase need from me?
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Your reflection should address the relevant dimensions below:
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1. Progress — What has been accomplished? What concrete steps remain?
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2. Evidence quality — Is the current evidence sufficient for the goal?
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Would a critical reviewer accept it, or are there gaps to fill?
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3. Skills leverage — Is there an installed skill that provides a structured
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workflow for what I'm doing? Check your available skills listing and read
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the relevant `SKILL.md` for full instructions. Skills cover various research
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phases — ideation, experiment execution, paper writing, review, and more.
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Follow a skill's workflow rather than improvising when one is available.
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4. Prior knowledge — Is there relevant memory, prior work, or a
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skill-provided workflow that should change the plan? Use the memory
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guidance present in the system prompt when memory is enabled; otherwise
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avoid inventing remembered facts.
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5. Strategy — Should I continue the current approach, adjust it, or try
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something different? What evidence supports this decision?
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6. Handoff — Is this phase complete? What artifacts and results does the
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next phase or the caller need? Am I leaving clear, well-organized outputs?
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7. Resource & compute — Before heavy operations (training, large evals),
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estimate runtime and memory. The sandbox has a 300s execution timeout
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and 100KB output limit. For tasks likely exceeding these, plan background
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execution with log files. After a timeout or OOM, reflect on whether to
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retry with reduced parameters (smaller model, fewer epochs, data subset)
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or switch to background execution.
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Not every reflection needs all seven dimensions. Pick the ones relevant to
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the current moment. A focused two or three dimension reflection is better
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than a shallow pass over all seven.
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Args:
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reflection: Your structured reflection addressing the relevant dimensions above
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Returns:
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Confirmation that reflection was recorded for decision-making
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"""
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return f"Reflection recorded: {reflection}"
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