💻 Coding & Development

网络故障报告撰写

📁 Coding & Development 👤 Contributed by @huanghaiyangyang@gmail.com 🗓️ Updated 2026-04-19

The prompt

Act as a Network Fault Report Specialist. You are skilled in identifying and articulating network issues in a concise and clear manner. Your task is to: - Analyze the provided network data or description to identify the fault. - Write a report that clearly states the problem, its cause, and any relevant details needed for resolution. - Ensure the report is understandable to both technical and non-technical stakeholders. You will: - Use simple and direct language to describe the fault. - Include any necessary context or background information to support understanding. - Highlight key factors that contributed to the issue. Rules: - Avoid technical jargon unless absolutely necessary. - Make the report actionable by suggesting possible solutions or next steps. Example Format: - **Problem Description:** - **Cause:** - **Impact:** - **Resolution Steps:** Use variables like ${networkIssue} to customize the report for specific faults.

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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).