jev-browser-use: 5–10x faster browser operations: Jev clicks, Codex thinks and verifies. Built at EZCollegeApp

by wy-coliney · Codex Skill · ★ 1.2k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install jev-browser-use

npx skills add wy-coliney/jev-browser-use -g -a codex -y
npx skills add wy-coliney/jev-browser-use

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 65/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars1,207
Forks34
LanguageJavaScript
CategoryCodex Skill
LicenseMIT
Quality Score65/100
Open Issues7
Last Updated2026-10-19
Created2026-09-18
Platformsbrowser, cli, codex, node
Est. Tokens~14k

About jev-browser-use

Jev Browser Use Jev clicks. Codex thinks and verifies. A browser Skill powered by TypeSafe’s Jev. Hand off navigation, clicks, toggles, and scrolling; keep Codex in charge of text input, visual judgment, and the final check. 5–10× faster browser operations in our EZCollegeApp workflows. Your existing browser connection. No extra driver or npm dependencies.

Compatible Skills

These tools work well together with jev-browser-use for enhanced workflows:

  • jev-router — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (57%)
  • awesome-jev-projects — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)
  • faster-chrome-devtools-skill — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)
  • jev-browser — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

View on GitHub → Browse Codex Skill tools