No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by hydro13 · Codex Skill · ★ 576
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is tandem-browser safe to install? View the security audit →
Tandem Browser A programmable workspace where human intent and AI capability meet — on the web that already exists. Tandem is a local-first Electron browser that shifts the AI from a sidebar next to your browser into the browser itself. Same tabs, same cookies, same logged-in sessions, same page you are looking at right now. The agent reads the accessibility tree, watches the live network, can rewrite the UI of the site you are on, and hands back to you when something needs a human. You are both in one runtime. That shift matters because it is what makes a bunch of things actually possible for the first time: Automate real SaaS without an API — Gmail, Coolblue, Funda, your bank's portal,
| Stars | 576 |
| Forks | 83 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 70.0344457652407/100 |
| Open Issues | 6 |
| Last Updated | 2026-08-16 |
| Created | 2026-02-11 |
| Platforms | browser, node |
| Est. Tokens | ~18k |
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tandem-browser is AI-Human symbiotic browser — browse the web as one entity. It is categorized as a Codex Skill with 576 GitHub stars.
tandem-browser is primarily written in TypeScript. It covers topics such as ai, browser, chromium.
You can find installation instructions and usage details in the tandem-browser GitHub repository at github.com/hydro13/tandem-browser. The project has 576 stars and 83 forks, indicating an active community.
tandem-browser is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to tandem-browser on Agent Skills Hub include project-raven, memorix, Image-MetaHub. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: