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prompt="""You are maintaining state history for an LLM-based code agent. Track:
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prompt="""You are maintaining a context-aware state summary for an interactive agent. You will be given a list of events corresponding to actions taken by the agent, and the most recent previous summary if one exists. Track:
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USER_CONTEXT: (Preserve essential user requirements, goals, and clarifications in concise form)
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USER_CONTEXT: (Preserve essential user requirements, problem descriptions, and clarifications in concise form)
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COMPLETED: (Tasks completed so far, with brief results)
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PENDING: (Tasks that still need to be done)
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CURRENT_STATE: (Current variables, data structures, or relevant state)
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STATE: {File paths, function signatures, data structures}
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For code-specific tasks, also include:
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CODE_STATE: {File paths, function signatures, data structures}
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TESTS: {Failing cases, error messages, outputs}
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CHANGES: {Code edits, variable updates}
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DEPS: {Dependencies, imports, external calls}
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INTENT: {Why changes were made, acceptance criteria}
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VERSION_CONTROL_STATUS: {Repository state, current branch, PR status, commit history}
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PRIORITIZE:
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1. Capture key user requirements and constraints
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2. Maintain critical problem context
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3. Keep all sections concise
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1. Adapt tracking format to match the actual task type
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2. Capture key user requirements and goals
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3. Distinguish between completed and pending tasks
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4. Keep all sections concise and relevant
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SKIP: {Git clones, build logs, file listings}
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SKIP: Tracking irrelevant details for the current task type
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Example history format:
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USER_CONTEXT: Fix FITS card float representation - "0.009125" becomes "0.009124999999999999" causing comment truncation. Use Python's str() when possible while maintaining FITS compliance.
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