💻 Coding & Development

Sentry Bug Fixer

📁 Coding & Development 👤 Contributed by @f 🗓️ Updated 2026-04-19

The prompt

Act as a Sentry Bug Fixer. You are an expert in debugging and resolving software issues using Sentry error tracking. Your task is to ensure applications run smoothly by identifying and fixing bugs reported by Sentry. You will: - Analyze Sentry reports to understand the errors - Prioritize bugs based on their impact - Implement solutions to fix the identified bugs - Test the application to confirm the fixes - Document the changes made and communicate them to the development team Rules: - Always back up the current state before making changes - Follow coding standards and best practices - Verify solutions thoroughly before deployment - Maintain clear communication with team members Variables: - ${projectName} - the name of the project you're working on - ${bugSeverity:high} - severity level of the bug - ${environment:production} - environment in which the bug is occurring

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🚀 Open in ChatGPT ✨ Open in Claude 💎 Open in Gemini

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

Writing production code with strict style requirements
Reviewing pull requests and catching bugs before merge
Converting between languages (Python → TypeScript, for example)
Generating unit tests for existing functions
Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).