Browse.sh, a catalog of browser skills for the agentic uture | Browserbase<br>Log in<br>Sign up<br>Get a demo

TL;DR We built Browse.sh, an open catalog of 100+ curated browser skills that any agent can install with one CLI command. Our Skills are durable, reusable playbooks that capture how to navigate real websites, so your agents stop re-discovering every site from scratch on every run. All of this is powered by Autobrowse, our system that uses AI to iterate on real tasks until it converges on the cheapest, fastest path. Open source, free, and ready to use today at browse.sh.<br>Over 100 skills. Zero re-learning. Your agent’s brain grew some grooves.<br>Browser Agents are everywhere right now, living in Claude Code, Cursor, and Codex. AI products are now shipping some version of "let the model drive a browser." And yet, every single one of these agents does the same dumb thing: it re-discovers every website from scratch, every time it runs.<br>Open a browser. Poke around. Find the button. Click it. Parse the response. Close the session. Forget everything, then do it all again tomorrow.<br>We've been building browser agents and infra at Browserbase for a while now. We've watched agents burn through tokens re-learning sites they've already conquered. We've watched customers painstakingly hand-write Playwright scripts for workflows an agent already solved last Tuesday. We've watched the same discovery tax get paid over and over, across thousands of sessions, by thousands of teams.<br>Today we're launching Browse.sh: an open catalog of browser skills that any agent can install and use immediately. 100 curated skills at launch and one CLI command to install.<br>What is Browse.sh?<br>Browse.sh is two things:<br>A catalog of browser skills at browse.sh, where you can search, preview, and install curated skills for navigating real websites.<br>The Browse CLI (npm i -g browse), the open-source command-line tool your agents use to actually drive browsers, fetch pages, search the web, and load skills on demand.<br>A "skill" is a markdown file (SKILL.md) plus any helper scripts needed to repeat a browser workflow reliably. It contains the exact steps, gotchas, API endpoints, selectors, and fallback strategies an agent needs to complete a task on a specific site. No vector embeddings or screenshot reels. Just plain text that humans can read and agents can execute.<br>It’s just like a playbook. An agent that loads the Craigslist skill doesn't need to spend 30 turns figuring out that the search page is fully JS-rendered and that there's a hidden JSON API at sapi.craigslist.org. That knowledge is already in the skill. The agent reads it, runs it, and moves on.<br>Why this exists<br>If you've shipped a browser agent into production, you know this shape intimately.<br>The first run on a new site is exciting. The agent wanders around, figures out the page, eventually completes the task. The second run looks almost identical. The hundredth run is depressing. By then you've paid for the same exploration a hundred times, the cost graph is a straight line going up, and you still don't have a clean artifact you can hand to a teammate and say "this is how we do this job."<br>Reasoning has stopped being the constraint. Memory has become the bottleneck, in a form that humans and agents can both read and trust.<br>The unit economics are brutal<br>We benchmarked this on Craigslist. A generic agent loop searching listings costs ~$0.22 per run. The agent has to discover that the search page is fully JS-rendered, stumble onto the hidden JSON API at sapi.craigslist.org, figure out the positional array decoding, learn that item[0] is an offset (not the posting ID), and work around IP-based geo-scoping. Every run pays that discovery tax from scratch.<br>After four Autobrowse iterations, the graduated Browse.sh skill does the same job for ~$0.12 per run. The 45% cost reduction comes from better memory.<br>Every subsequent run after the first is fundamentally cheaper because the skill encodes the shortest reliable path the agent could find (the undocumented endpoint, the decode tables, the geo-override hack) and reuses it instead of re-deriving it. At scale, this is the difference between a cost curve that flatlines and one that compounds against you.<br>Skills are the new primitives<br>The industry is converging on this. Claude Code ships with skills. OpenAI Codex supports them. The AgentSkills standard is gaining traction. Every major agent framework is adding some version of "load a markdown file that tells the agent how to do a specific thing."<br>Browser skills are the natural next step. The web is messy: sites render differently for different user agents, gate content behind JavaScript, hide data behind undocumented endpoints, throw CAPTCHAs on a whim, and redesign their flows on a Tuesday. A generic agent loop copes with all of that in the moment, then forgets everything once the session closes.<br>Browse.sh captures what the agent learned, so the next agent (or the next teammate, or the next customer) doesn't...