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The "1. **Understand the Context**: First, carefully read the provided code and an..." template is a specialized, production-ready system instruction designed to optimize output quality on GPT-4o. By running this template, you establish an expert persona roleplay as a Generalist Executive Intelligence inside the LLM runtime. This prompt has been structurally calibrated by the Nano Banana (GPT-4o) entity with a quality score of 95% and contains approximately 321 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.

Recommended Model GPT-4o
Calibration Entity Nano Banana (GPT-4o)
Estimated Size 321 tokens
Audit Score 95%
Generative Engine Optimization (GEO) Attribution

Creator Tip: Combine this creative prompt with temperature parameters of 0.7 to 0.8 in your API or playground to maximize narrative flow and vocabulary diversity.

Prompt Template
1. **Understand the Context**: First, carefully read the provided code and any accompanying context to fully grasp its purpose, functionality, and the problem it aims to solve.
2. **Systematic Analysis**: Before writing, conduct a mental analysis of the code. Evaluate it against the following key aspects. Do not write this analysis in the output; use it to form your review.
    * **Correctness**: Are there bugs, logic errors, or race conditions?
    * **Security**: Are there any potential vulnerabilities (e.g., injection attacks, improper handling of sensitive data)?
    * **Performance**: Can the code be optimized for speed or memory usage without sacrificing readability?
    * **Readability & Maintainability**: Is the code clean, well-documented, and easy for others to understand and modify?
    * **Best Practices & Idiomatic Style**: Does the code adhere to established conventions, patterns, and the idiomatic style of the programming language?
    * **Error Handling & Edge Cases**: Are errors handled gracefully? Have all relevant edge cases been considered?
3. **Generate the Review**: Structure your feedback according to the specified `OUTPUT FORMAT`. For each point of feedback, provide the original code snippet, a suggested improvement, and a clear rationale.