DeepSeek Prompt for DeepSeek Optimized
A DeepSeek-optimized debugging prompt for identifying and fixing data corruption issues in a Python project management tool.
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I need help debugging and fixing a data corruption issue in my Python project management tool project. **Leverage DeepSeek strengths:** Debugging requires systematic reasoning, understanding complex code interactions, and producing correct fixes, which are your strengths. **Tech stack:** Next.js + TypeScript **My experience level:** full-stack developer ## Debugging Session Framework ### Step 1: Problem Description Template Help me describe the bug systematically: - **Expected behavior:** What should happen - **Actual behavior:** What is happening instead - **Reproduction steps:** Minimal steps to trigger the issue - **Frequency:** Always, intermittently, or under specific conditions - **Environment:** Development, staging, or production - **Recent changes:** What changed before the bug appeared ### Step 2: Diagnostic Approach for data corruption Issues Walk me through a systematic debugging process: 1. What information to gather first 2. Which logs, metrics, or traces to examine 3. How to narrow down the root cause 4. Common causes of data corruption issues in Python with Next.js + TypeScript 5. Debugging tools and commands specific to this stack ### Step 3: Common data corruption Patterns in Python List the top 10 most common causes of data corruption bugs in Python applications: For each pattern: - Description of the bug pattern - Code example showing the buggy code - Explanation of why it causes problems - Corrected code with the fix - How to prevent it in the future (linting rules, testing, code review checklist) ### Step 4: Fix Implementation Guide When I share my specific code, analyze it for: - The root cause of the data corruption issue - Any related bugs that might be lurking nearby - The minimal fix that resolves the immediate issue - A more thorough fix that prevents the class of bugs - Regression test to ensure the bug does not return ### Step 5: Prevention Strategies - Testing patterns that would have caught this data corruption bug - Static analysis or linting rules to add - Code review checklist items for data corruption issues - Monitoring and alerting to detect this issue in production - Architectural patterns that make data corruption bugs less likely ### Step 6: Knowledge Capture - Document the root cause and fix for the team - Create a runbook for diagnosing similar issues - Update development guidelines to prevent recurrence Please share your code and error messages, and I will walk through this framework with you step by step.