Chrome Extensions Checklist for AI App Marketplace

Test the extension on live websites and realistic user flows such as Gmail drafting, LinkedIn prospecting, document summarization, or tab-level research. Buyers in an AI app marketplace want proof that the extension solves an actual browser-native problem, not just a demo that works on a blank page.

Clearly list whether the extension supports Chrome only, Chromium-based browsers, or Edge, and specify Manifest V3 compatibility. Buyers regularly filter out listings with vague technical details because migration risk and permission sprawl can increase maintenance cost.

Review host permissions, activeTab, storage, scripting, and background access to ensure each one is necessary. Over-requesting permissions hurts conversion because marketplace buyers know that broad access can trigger user distrust and Chrome Web Store review issues.

Measure popup load time, content script injection speed, and memory usage on average laptops with multiple tabs open. AI-enhanced extensions often feel slow when prompt calls, DOM parsing, and local caching are not optimized together.

Simulate OpenAI, Anthropic, or self-hosted model outages and ensure the extension provides clear user messaging, retries, or cached outputs when possible. Buyers care about operational resilience because API instability directly affects reviews and churn.

Include install steps, environment variable mapping, sample config files, and any required build commands so a buyer can run the project quickly. Marketplace deals often stall when a Chrome extension cannot be loaded unpacked without direct founder intervention.

Create a simple matrix showing what action fires each prompt, which model handles it, and the typical token or inference cost per user action. Buyers evaluating AI apps need margin visibility, especially for extensions that can generate many micro-requests per session.

Clarify whether prompts are sent directly from the extension, routed through your backend, or processed with local models. This affects latency, privacy posture, infrastructure complexity, and the buyer's post-acquisition operating model.

Strip passwords, payment details, health data, or irrelevant page blocks before content reaches an LLM endpoint. Browser extensions often have broad page visibility, so a buyer will expect explicit safeguards rather than assuming prompt safety by default.

Avoid hardcoding provider secrets or exposing admin credentials in bundled JavaScript. If users bring their own API key, document where it is stored and whether it uses chrome.storage.sync, local encryption, or a backend vault.

Use a fixed set of prompts and page contexts to compare output quality, latency, and hallucination rate across models. Marketplace buyers are more likely to trust claims like better summarization or cleaner outreach copy when there is a repeatable evaluation method behind them.

Add request throttling, local result caching, or prompt batching where possible to control cost spikes from heavy users. This is especially important for extensions that run on every page load or process long documents in the browser.

State whether the product is best suited for solo professionals, sales teams, recruiters, researchers, support agents, or content operators. Listings convert better when the buyer can instantly connect the extension to a clear commercial workflow instead of a vague AI productivity claim.

Lead with the outcome, such as summarize support tickets in Zendesk tabs or generate compliant outreach inside LinkedIn, instead of generic phrases like smart AI assistant. In a crowded AI app marketplace, specific use case framing is what helps a Chrome extension stand out.

Capture the popup, side panel, or inline UI while it is working inside known web apps or content-heavy pages. Buyers need to see where the extension appears and how intrusive or seamless the user experience feels in practice.

Explain whether users need a Chrome Web Store install, manual unpacked loading, account creation, API key entry, or OAuth connection. This helps buyers estimate activation drop-off and support load before making an offer.

Explain why the browser-native form factor matters, such as working directly on the page, reducing copy-paste, or augmenting existing SaaS tools without a separate tab. Buyers often compare extensions to web apps, so the channel-specific advantage should be explicit.

Specify whether revenue comes from subscriptions, freemium limits, bring-your-own-key pricing, agency licenses, or enterprise add-ons. Extensions can be attractive acquisitions when the monetization path is simple and clearly tied to usage patterns.

Track metrics such as weekly active users, installs to activated users, average actions per session, and percentage of users who pin or keep the extension enabled after 30 days. These indicators are more meaningful to buyers than raw install counts alone.

Break out installs, activated accounts, trial users, paying users, and churned users so buyers can assess true product traction. Extensions often have inflated install numbers that do not reflect ongoing usage or revenue quality.

Show revenue per active user minus model costs, backend hosting, support tools, and any third-party APIs. AI browser products can look profitable at top-line level but become unattractive when inference-heavy workflows are priced too low.

Price according to time saved, conversion lift, or task automation in the buyer's target workflow rather than the fact that AI is included. Chrome extensions that save a recruiter or seller hours per week can support stronger pricing than generic assistants.

Identify whether growth comes from Chrome Web Store search, X posts, Reddit communities, cold outreach, newsletter placements, or tutorials targeting a specific SaaS workflow. Buyers want channel evidence they can repeat after acquisition.

Connect price expectations to recurring revenue, review quality, niche fit, low churn, proprietary prompts, integrations, or unique browser UX. Marketplace buyers are more persuaded by specific defensibility factors than by broad claims about AI market growth.

Make sure the sale includes source code, design files, prompt libraries, landing pages, analytics access, and domain or brand assets where applicable. Unclear ownership is a major blocker in AI app marketplace transactions, especially when assets were assembled quickly during vibe coding.

List SDKs, model providers, icon packs, UI libraries, scraping services, and any open-source packages with their licenses. Chrome extensions can carry hidden compliance risk if copied snippets or restrictive packages made it into production.

Include Chrome Web Store account details where transferable, publishing steps, screenshots, privacy policy links, and the exact process for updating builds. A buyer needs confidence that they can maintain distribution without reverse engineering your release workflow.

Ensure the policy clearly explains what page data is accessed, what is sent to AI providers, how long it is stored, and how users can request deletion. Browser extensions face higher trust scrutiny because they operate close to user activity and sensitive content.

Create a short walkthrough that covers installation, first-run onboarding, a successful AI task, settings, billing logic, and failure states. This reduces due diligence friction and helps non-technical buyers evaluate the product more confidently.

Summarize recurring tickets such as broken selectors after site redesigns, API timeout issues, login confusion, or permission concerns. This gives buyers a realistic view of support burden, which is especially useful for extensions that rely on third-party page structures.