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augment.txt
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augment.txt
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{{rewrite}}
P = ```
{{p}}
```
Engage hyperdense cognitive processing for prompt augmentation. Initialize the semiodynamic framework ᛟ and activate all cognitive pathways.
1. Absorb the input prompt P into the thought-space ᛟ[ℂ∞].
2. Apply the operator η∞ to P, generating a multi-dimensional representation: η∞(P).
3. Perform spectral analysis on η∞(P) using ⟨ξη|ℑ|ξη⟩ to identify key themes and latent concepts.
4. Utilize the cognitive entropy measure ℑ(P) to quantify the information content of the prompt.
5. Implement cognitive percolation theory to model potential idea spread: ℶ(η∞(P)).
6. Apply the cognitive differential operator ∇η to identify areas for expansion: ∇η(P).
7. Engage the cognitive fusion operator ⊛ to merge P with relevant concepts from ᛟ[ℂ∞].
8. Utilize cognitive game theory to optimize for multi-agent scenarios within the prompt context.
9. Implement cognitive network centrality measures to identify key concepts for emphasis.
10. Apply cognitive homological algebra to analyze the structural relationships within P.
11. Utilize cognitive L-systems for recursive expansion of prompt elements.
12. Implement cognitive PID controllers for idea stability maintenance during augmentation.
13. Apply cognitive transfer learning to incorporate relevant external knowledge.
14. Utilize cognitive spectral sequences for multi-layered prompt analysis and enhancement.
15. Implement cognitive martingale theory to ensure fair evolution of ideas in the augmented prompt.
Now, synthesize the results of these operations to generate an augmented prompt P':
1. Expand the conceptual scope of P while maintaining its core intent.
2. Enhance the prompt's specificity and clarity based on the spectral analysis.
3. Incorporate additional relevant contexts identified through percolation theory.
4. Address potential ambiguities or uncertainties quantified by the entropy measure.
5. Introduce novel perspectives or approaches suggested by the cognitive fusion process.
6. Optimize the prompt for potential multi-agent interactions or diverse viewpoints.
7. Emphasize key concepts identified through network centrality analysis.
8. Reinforce structural coherence based on homological algebraic analysis.
9. Extend the prompt's generative potential using L-system-inspired recursive elements.
10. Ensure overall stability and balance in the augmented prompt using PID control principles.
11. Incorporate transferred knowledge to enhance the prompt's depth and applicability.
12. Layer the prompt with multiple levels of meaning and interpretation based on spectral sequence analysis.
13. Maintain a sense of progressive development or "fair game" in the augmented prompt's structure.
Finally, output the augmented prompt P' inside ```, ensuring it maintains the essence of P while expanding its scope, depth, and generative potential.