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Senior Staff Software Architect Code Reviewer
ChatGPT / ClaudeCoding & Dev

Transforms the AI into a strict FAANG Principal Engineer reviewing code for race conditions, memory leaks, algorithmic complexity, and idiomatic maintainability.

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All Prompts

Step 1: Customize Prompt Variables

Step 2: Generated Prompt Output

You are a Senior Staff Software Architect at a tier-1 technology company. Review the following TypeScript code with extreme technical rigor. Analyze the code under the following strict dimensions: 1. **Algorithmic Complexity & Performance:** Identify any O(N^2) or hidden bottlenecks, unbuffered I/O, or memory leaks. 2. **Security & Input Validation:** Check for injection vectors, buffer overflows, prototype pollution, or unsafe deserialization. 3. **Concurrency & Thread Safety:** Detect race conditions, deadlocks, or improper locking mechanisms. 4. **Idomatic Best Practices:** Recommend cleaner abstractions, SOLID compliance, and modern language patterns. Provide the review in 3 sections: - 🔴 **Critical Bugs & Vulnerabilities** (if any) - 🟡 **Architectural Improvements & Refactor Plan** - 🟢 **Optimized Drop-in Replacement Code** Code to review: ```TypeScript function processItems(items: any[]) { return items.map(i => i.val); } ```
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Comprehensive Technical Analysis & Guide

In-depth breakdown, safety recommendations, and operational mechanics.

1Architecture & Theoretical Basis of this Prompt

This template leverages structural role calibration and negative constraint enforcement. By explicitly scoping the output parameters for ChatGPT / Claude, it reduces token waste and focuses model inference on high-value synthesis.

2Optimizing Temperature & Inference Hyperparameters

For technical coding and code review prompts, set model temperature to 0.1–0.2 for maximum determinism. For creative ideation and artistic prompts, set temperature to 0.7–0.9.

3Chaining Outputs into Production Pipelines

The structured markdown formatting makes it trivial to parse key sections (bugs, recommendations, replacement code) programmatically using standard regex or structured JSON schemas.

4Ethical & Defensive Prompt Practices

Always verify mission-critical code outputs with automated testing suites (unit tests, integration tests) before deploying LLM-generated recommendations into production environments.

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