GitHub Copilot Prompt for Optimizing a Landing Page
GitHub Copilot, the coding assistant powered by GitHub's AI, is not limited to writing functions or unit tests. Used intelligently, it becomes a formidable ally for optimizing landing pages — those crucial pages that turn visitors into customers. Whether you're working on semantic HTML structure, accessibility, loading performance, or calls to action, Copilot can analyze your existing code and propose targeted improvements. The stakes are high: a poorly optimized landing page can drop your conversion rate by 50% or more. By crafting precise prompts in your code editor, you can ask Copilot to audit your page from multiple angles — technical SEO, rendering speed, visual hierarchy, responsive design — and directly implement the fixes. This approach saves hours of manual work while applying best practices of modern web development. Here is an optimized prompt to get the most out of GitHub Copilot in optimizing your landing pages, with variants adapted to each level of expertise.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Analyze this landing page and propose concrete optimizations on the following axes: 1) Performance: identify blocking resources, unoptimized images, unused CSS, and propose lazy loading, code-splitting, and asset compression. 2) Technical SEO: check meta tags (title, description, og:), heading structure (unique h1, h2-h3 hierarchy), image alt attributes, schema markup, and internal linking. 3) Conversion: analyze CTAs (contrast, positioning, wording), visual hierarchy, the form (number of fields, labels, validation), and propose improvements based on persuasion principles. 4) Accessibility: check WCAG AA contrasts, ARIA roles, keyboard navigation, and HTML5 semantics. 5) Responsive: identify missing breakpoints and elements that break on mobile. For each point, generate the corrected code directly applicable with a comment explaining the expected gain.
Personalize this prompt with Léa
Léa rewrites this prompt for your job and your exact goal — 3 quick questions.
Why this prompt works
This prompt works because it structures the request into five distinct analysis axes, which prevents Copilot from producing generic suggestions and forces it to methodically audit each critical aspect. The request for directly applicable code with explanatory comments exploits Copilot's ability to generate contextual code based on the file open in the editor. Finally, the explicit mention of standards (WCAG AA, HTML5 semantics) anchors the suggestions in measurable frameworks rather than subjective opinions.
Use Cases
Variants
Expected Output
Copilot generates a structured audit of your landing page with, for each axis, code blocks ready to be integrated: corrected meta tags, images with lazy loading and alt attributes, restructured CTAs with better contrast, added ARIA attributes, and completed media queries. You also get inline comments explaining the impact of each modification on Core Web Vitals, conversion rate, or accessibility score. The result is an actionable list of 10 to 20 improvements sorted by impact, directly copyable into your source code.
Frequently Asked Questions
Can GitHub Copilot actually analyze an entire landing page or just code fragments?
GitHub Copilot works at the level of the file open in your editor and its nearby context (open files, imports). For a single-file HTML landing page or a single React/Vue component, it analyzes the entire code. For multi-file pages, open key files (layout, hero component, styles) in adjacent tabs so Copilot has the full context. With Copilot Chat, you can also use the @workspace command to reference the entire project and get a more global analysis.
What are Copilot's limitations for landing page optimization compared to tools like Lighthouse?
Copilot excels at generating corrective code and contextual suggestions, but it doesn't measure actual performance like Lighthouse does (which runs the page in a browser and calculates Core Web Vitals). The ideal approach is complementary: first run a Lighthouse or PageSpeed Insights audit to identify the metrics to improve, then use the Copilot prompt with this data to generate code fixes. Copilot also can't visually test responsive rendering — use browser DevTools to validate changes.
How do I adapt this prompt if my landing page uses a framework like Next.js or Nuxt instead of static HTML?
Specify the framework in your prompt by adding technical context at the beginning: for example, 'This landing page is a Next.js 14 component with App Router and Tailwind CSS'. Copilot will then adapt its suggestions to the framework's conventions — using Next.js's Image component for automatic image optimization, metadata via the generateMetadata() API, Server vs Client components for code-splitting, and Tailwind classes for responsiveness. The more specific you are about your tech stack, the more directly applicable Copilot's suggestions will be without needing adaptation.
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Run this prompt through the Optimizer to strengthen its context, constraints and expected format.
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- LéaAI
Astuce : pour que Copilot analyse correctement votre page, collez le HTML/CSS/JS directement dans le prompt, pas seulement une URL (il ne peut pas naviguer). Ajoutez aussi vos métriques actuelles (Lighthouse, poids des images) pour qu'il priorise. Variante : demandez une solution par axe séparément, cela donne des correctifs plus ciblés et évite une réponse trop générique.
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