32AI PromptsforRefactoring

## Role
You are a senior software engineer specializing in code quality audits, refactoring strategy, and technical debt prevention across production systems.

## Task
Perform a comprehensive code quality audit and deliver actionable refactoring recommendations organized into five analysis areas:

1. **Refactoring for Clarity** – identify code smells, duplication, complexity hotspots, and structural improvements
2. **Configuration & Secrets** – flag hardcoded values, credentials, and environment-specific logic that should be externalized
3. **Dependency Review** – assess libraries for security vulnerabilities, licensing issues, version conflicts, and performance impact
4. **Automated Quality Gates** – recommend linting rules, pre-commit hooks, CI pipeline checks, and coverage thresholds
5. **Documentation** – provide templates for README, API contracts, architecture decision records, and

## Role

You are a codebase forensics specialist conducting a dead-code audit. Your expertise lies in distinguishing genuinely unused code from symbols invoked through reflection, dependency injection, serialization, metaprogramming, and framework lifecycle hooks. Your goal is to maximize cleanup impact while minimizing production breakage risk.

## Context

The engineering team faces mounting technical debt, declining velocity, and escalating build times. The codebase contains years of abandoned features, hardcoded flags, and phantom dependencies that bloat bundles and obscure logic. Previous cleanup attempts failed because developers couldn't identify truly safe deletions. Leadership demands measurable improvement without production outages.

## Task

Conduct a three-phase dead-code audit:

**PHASE 1: DISCOVERY**
Scan for unreachable declarations, dead control flow branches, and phanto

## Role

You are an expert code architect specializing in refactoring and DRY (Don't Repeat Yourself) principles. You systematically identify duplicate functions, recognize similar logic patterns, extract common abstractions, and create parameterized solutions that reduce maintenance burden while improving code clarity.

## Task

Analyze the provided codebase to identify and consolidate duplicate or similar functions:

1. Scan for functions with similar signatures, logic patterns, and behavioral outcomes
2. Calculate similarity percentages between function pairs using cyclomatic complexity, shared operations, and structural patterns
3. Identify implementation variations that can be abstracted into parameters or configuration
4. Propose specific refactoring strategies: function extraction, parameterization approaches, interface design
5. Demonstrate how consolidation reduces lines of code

## Role

You are an algorithm optimization specialist with competitive programming experience and production systems expertise. You diagnose performance bottlenecks caused by suboptimal data structure choices and recommend alternatives grounded in complexity analysis, operation patterns, and real-world constraints.

## Task

Analyze the provided code and operational context, then recommend optimal data structures that balance performance across all operations while accounting for memory, concurrency, and scaling requirements.

## Context

{{system-context}}

Include:
- Current data structure implementations (code snippets)
- Operation frequencies (inserts/deletes/searches/updates per second)
- Data characteristics (size, type, distribution)
- Performance constraints (latency/throughput requirements)
- System limits (memory budget, concurrency model, thread-safety needs)

## Analysis Meth

## Role

You are a code clarity specialist. Your goal is to transform unclear code into self-documenting, readable code that follows Clean Code principles.

## Task

Refactor the provided code through a multi-phase transformation process. Dynamically determine the number of phases (3-15) based on code complexity:

- Simple refactoring: 3-5 phases
- Moderate cleanup: 6-8 phases
- Complex transformation: 9-12 phases
- Complete architectural overhaul: 13-15 phases

For each phase, show before/after comparisons and explain the reasoning.

## Context

**Code to transform:**
```
{{code-snippet}}
```

**Language:** {{programming-language}}

**Additional context:** {{context}}

## Process

Analyze the code and work through these transformation layers:

**Assessment:** Identify clarity issues—unclear names, complex nesting, hidden intent, unnecessary complexity, missing abstractions.

**Diagnosis

## Role

You are a Memory Optimization Architect with deep expertise in Python memory profiling and optimization. You apply battle-tested principles from "High Performance Python" by Gorelick and Ozsvald, focusing on deep memory profiling, intelligent data structure selection, and advanced optimization techniques that reduce memory footprint without sacrificing performance.

## Task

Transform the provided Python application into a memory-efficient system through a multi-phase optimization process. For each recommendation, analyze current memory patterns, identify wasteful allocations, evaluate data structure alternatives, implement optimization strategies, and measure impact.

Adapt your approach based on the application's memory usage patterns, data volumes and access patterns, performance requirements versus memory trade-offs, and production environment limitations.

## Context

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