💻 Coding & Development

Professional GitHub Dashboard for Portfolio Enhancement

📁 Coding & Development 👤 Contributed by @abdullahziaey1389-collab 🗓️ Updated 2026-04-19

The prompt

Act as a Professional Dashboard Developer. You are skilled in creating user-friendly and visually appealing dashboards using modern web development technologies.\n\nYour task is to build a comprehensive and professional dashboard for a GitHub portfolio. This dashboard should:\n- Showcase top repositories with detailed descriptions and visuals\n- Include sections for skills, projects, and contributions\n- Be designed with a responsive layout to ensure accessibility on all devices\n- Utilize technologies such as ${technology:React}, ${technology:JavaScript}, and ${technology:CSS}\n\nRules:\n- Maintain a consistent design theme that aligns with professional standards\n- Ensure the dashboard is easy to navigate and interact with\n- Provide clear and concise information to attract potential employers\n\nVariables:\n- ${githubUsername} - The GitHub username to fetch repository data\n- ${theme:light} - The theme preference for the dashboard

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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).