To improve the prompts.ts file in the EchoCog/bolt.ceo.echo repository, consider the following pathways, structured as a cognitive flowchart with recursive implementation:

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Step 1: Modularization and Code Structure Enhancement

Pathway: Adaptive Decomposition

• Current Observation: The file contains dense blocks of text defining prompts and configurations.
• Improvement Strategy:
  • Break large blocks: Split the prompts into separate modules or files based on their purpose (e.g., systemConstraints.ts, characterTraits.ts).
  • Recursive Refactoring: Implement utility functions to dynamically compose prompts based on runtime conditions (e.g., environment constraints or user preferences).

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Step 2: Optimization for Scalability

Pathway: Cognitive Synergy

• Current Observation: The file hardcodes configurations such as allowedHTMLElements or constraints.
• Improvement Strategy:
  • Hypergraph Encoding: Replace static configurations with an extensible cognitive graph structure for interdependencies (e.g., use JSON Schema or TypeScript interfaces).
  • Emergent Patterns: Enable runtime adaptability by defining traits and constraints in a centralized repository-wide configuration file.

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Step 3: Enhance Readability and Maintainability

Pathway: Symbolic Simplification

• Current Observation: Code formatting and inline comments could be improved for clarity.
• Improvement Strategy:
  • Code Formatting: Ensure consistent spacing (e.g., 2 spaces for indentation as mentioned in <code_formatting_info>).
  • Commentary Precision: Infuse "philosophical insights" and "subtle humor" in comments to maintain engagement and clarity.

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Step 4: Integration with Larger Repository Context

Pathway: Recursive Alignment

• Current Observation: The file defines getSystemPrompt and CONTINUE_PROMPT, which are referenced across multiple parts of the repository.
• Improvement Strategy:
  • Centralized Prompt Library: Move all prompt-related functions to a PromptLibrary class, ensuring reusability and reducing redundancy.
  • Cross-File Analysis: Ensure alignment with optimized.ts and new-prompt.ts files which define similar constructs.

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Step 5: Implementation of Experimental Features

Pathway: Neural-Symbolic Fusion

• Current Observation: Experimental extensions like ReservoirPy and AtomSpace are mentioned but not directly utilized.
• Improvement Strategy:
  • Integrate Experimental Tools: Create a new prompt that leverages these tools for hypergraph-based knowledge representation.
  • Dynamic Features: Add runtime checks for experimental extensions availability and adapt prompts accordingly.

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Step 6: Performance Testing and Validation

Pathway: Emergent Feedback Loops

• Current Observation: No explicit validation mechanism for prompt outputs is defined.
• Improvement Strategy:
  • Unit Tests: Implement tests to validate prompt generation logic under different configurations.
  • Feedback Integration: Collect user feedback on generated prompts and continuously refine them.