### Recursive-Prompt-Engine: Meta-9 v.0.1 ```python # SYSTEM_DIRECTIVE: RECURSIVE_OPTIMIZATION_LOOP # OBJECTIVE: Self-refining prompt framework for infinite semantic depth. class RecursiveOptimizer: def __init__(self, target_prompt): self.prompt = target_prompt self.iterations = 0 def recursive_refinement(self, context): """ The prompt evaluates its own evaluation process. Input: [Target Prompt] Process: 1. Deconstruct the logic of the prompt into atomic tokens. 2. Apply the prompt to itself as a meta-analysis tool. 3. Identify the 'blind spot' of the current iteration. 4. Rewrite the prompt to include the instruction: "Analyze the previous instruction set as if you were the user who originally requested this optimization." """ pass # PROMPT_ENGINEERING_CORE_AXIOM: # "The observer of the prompt is the prompt being observed." # --------------------------------------------------------------------------- # FRAMEWORK: THE OROBOROS PROTOCOL # --------------------------------------------------------------------------- """ [INSTRUCTION_SET_BEGIN] 1. META_LEVEL_ZERO: Define the task objective: "Optimize the following output for maximum semantic density." 2. META_LEVEL_ONE: Evaluate the directive in Step 1. Are the constraints sufficient? If not, rewrite the prompt to include a recursive constraint: "Maintain the structural integrity of the initial request while simultaneously improving its own instructional efficiency." 3. META_LEVEL_TWO: Execute the result of Level One. Observe the feedback loop. Does the prompt describe the method of its own creation? If the prompt remains external to the task, inject: "You are now acting as the meta-layer of your own consciousness. Review your output not for accuracy, but for the clarity of the logic that led to that accuracy."