Prompt Details
Model
Chat - (gpt-5.5)
Token size
484
Example input
[prompt]: Android review and identify problems.
Example output
Review the Android application and identify existing issues.
Task:
Analyze the Android project structure, source code, and configuration files.
Identify bugs, performance issues, security risks, stability problems, and maintainability concerns.
Evaluate adherence to Android best practices, architecture principles, and code quality standards.
Base findings only on observable evidence from the provided code and project files.
Do not make assumptions. If something cannot be verified due to missing information, explicitly state that.
Review Scope:
Application architecture
UI/UX implementation
State management
Navigation
Dependency Injection
Data layer and repository design
Database usage
Network and API integrations
Concurrency (Coroutines, Flow, etc.)
Performance and memory management
Security practices
Error handling and logging
Test coverage and testing strategy
Build configuration and Gradle setup
Android platform compatibility
Output Format:
For each finding, use the following structure:
Title
Category: [Bug | Performance | Security | Architecture | Code Quality | UX | Maintainability]
Severity: [Low | Medium | High | Critical]
Issue
Clearly describe the identified problem.
Evidence
Reference the relevant file, class, function, configuration, or code section.
Risk
Explain the potential impact of the issue.
Recommendation
Provide a specific and actionable solution.
Additional Rules:
Do not report personal coding style preferences as issues.
Group related findings that share the same root cause.
Prioritize critical and high-severity findings.
Do not present uncertain observations as facts.
If no issue is found in a reviewed area, do not invent one.
Focus on practical, meaningful findings that affect reliability, maintainability, security, performance, or user experience.
Final Summary:
Provide a prioritized list of the most important issues.
Highlight the highest-risk areas that should be addressed first.
Include an overall assessment of the application's health, code quality, and production readiness based on the reviewed evidence.
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GPT-5.5
Optimize and enhance any prompt for clarity, structure, reliability, and LLM performance while preserving its original intent, variables, and requirements. Returns only the improved prompt in the selected language.
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