Chrome Extensions Checklist for Vibe Coding

Pick a single, high-frequency task such as summarizing pages, auto-filling forms, or extracting leads from a tab. Vibe-coded extensions fail when prompts try to generate a dashboard, API layer, and browser workflow all at once.

Describe what the user clicks, what the extension reads, what it changes, and what success looks like. This gives your AI assistant a grounded workflow for generating popup, content script, and background logic without inventing extra features.

Chrome extensions now center on Manifest V3, including service workers instead of persistent background pages. If your prompts generate MV2 patterns, you will hit broken APIs and store rejection later.

Decide exactly whether you need activeTab, storage, scripting, tabs, or host permissions before code generation. Overbroad permissions are a common problem in AI-produced extension manifests and can trigger user distrust or review issues.

Know whether this extension will be sold as a one-time product, used as a lead magnet, or bundled into a freelance service. That decision shapes whether you need local-only functionality, account sync, or usage gating from day one.

Save URLs or HTML snapshots that reflect messy real-world layouts, not idealized demo pages. These examples help you test whether AI-generated selectors, DOM parsing, and injected UI survive actual websites.

Ask the AI to output manifest.json, popup.html, popup.js, content.js, background.js or service-worker.js, styles, and any options page separately. This reduces the common vibe coding issue where everything gets merged into one unusable blob.

Have the model justify use of chrome.storage, chrome.tabs, chrome.scripting, or chrome.runtime messaging. This catches hallucinated APIs and helps non-technical builders understand where failures are likely to occur.

Many extensions break because the AI writes isolated scripts with no communication plan. Explicitly prompt for event flow, payload shape, and error handling between browser contexts.

Chrome extension security rules and content security policies often block casual web app patterns. Prompt for external script files, sanitized DOM updates, and no eval-style logic to prevent review and runtime problems.

If your extension reads page content, ask the AI to include resilient selector logic, text-based fallbacks, and graceful failure states. This matters because vibe-coded DOM extraction often works on one page and collapses on the next layout variation.

You do not need comments everywhere, but popup state changes, async message handling, and script injection points should be labeled. This makes it easier to repair AI-generated code when outputs drift after later prompts.

Check version, action config, permissions, host_permissions, icons, and service worker references yourself. A single malformed key in manifest.json can make an otherwise solid extension fail to load.

AI-generated popups often assume perfect data and instant responses. Add visible states for no active tab data, API failures, permission denial, and long-running tasks so users do not think the extension is broken.

Confirm your matches patterns and injected logic do not fire across unrelated domains. Over-triggering scripts hurts performance and creates privacy concerns, especially for extensions built quickly through conversational prompts.

Whether users choose prompts, output format, or target websites, persist preferences in extension storage rather than hardcoding values. This is especially useful when selling a polished tool instead of a prototype.

If the extension sends text to an LLM or external API, prevent repeated clicks from firing duplicate requests. This protects your costs and stops the messy user experience common in first-pass vibe-coded assistants.

Do not assume scraped text exists or that JSON responses always return the same shape. Add checks before reading nested fields so one malformed page or model response does not crash the full extension flow.

Open the popup console, inspect the content script on the page, and review the service worker logs in chrome://extensions. Vibe coders often debug only the visible UI and miss message passing or background execution failures.

When something breaks, provide the failing file, exact error message, reproduction steps, and expected behavior in one structured prompt. This produces better fixes than repeatedly asking the model to guess what went wrong.

Run the extension across different layouts, logged-in and logged-out states, and pages with missing elements. Browser tools built through vibe coding usually overfit to the first example page used in prompting.

Use Chrome DevTools throttling or temporary endpoint failures to see how your extension behaves under stress. If your tool depends on AI output, these conditions reveal where users will get stuck waiting without feedback.

Some AI-generated flows work once and fail after navigation because listeners are not reattached or cached state disappears. Verify the extension still responds correctly when tabs reload, switch, or close.

Use identifiable tags such as popup-init, content-parse, or api-request-fail in logs. This makes iterative debugging much faster when your AI assistant helps you patch specific failure points.

Write a plain-language justification for every sensitive permission and match it to actual extension behavior. This helps with review and gives future buyers confidence that the tool is not overreaching.

Do not just show the popup UI. Capture before-and-after states on an actual webpage so potential users understand the outcome, especially if you plan to sell the extension as a finished productivity tool.

A one-minute setup guide reduces support requests from users who do not know they must pin the extension, grant access, or open a supported site first. This is vital for non-technical buyers.

Because vibe-coded products can change rapidly after each AI iteration, track what was modified and why. This helps you avoid reintroducing old bugs and gives customers visibility into active maintenance.

Before publishing or selling, strip out duplicate scripts, abandoned components, and temporary API keys. AI-assisted builds often accumulate leftover code that makes handoff and review much harder.

Link to a form, email, or lightweight issue tracker from the extension or listing page. Early users will uncover edge cases that no prompt session or local test cycle could fully predict.