bridgic-browser: LLM-driven browser automation library built on Playwright with 67 CLI/SDK tools, stable snapshot refs

by bitsky-tech · Agent Tool · ★ 75

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

How to install bridgic-browser

npx skills add bitsky-tech/bridgic-browser --skill bridgic-browser
pip install bridgic-browser

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 67/100

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.

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.

agent-toolsai-agentbrowser-automationclillmplaywrightpython

Quick Facts

Stars75
Forks7
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score67/100
Open Issues1
Last Updated2026-05-19
Created2026-02-03
Platformsbrowser, cli, python
Est. Tokens~878k

About bridgic-browser

English | 中文 Bridgic Browser Bridgic Browser is a Python library for LLM-driven browser automation built on Playwright. It includes CLI tools, Python tools and skills for AI agents. Features Comprehensive CLI Tools - 67 tools organized into 15 categories; Designed to integrate with any AI agent Python-based Tools - Used for agent / workflow code generation; Easier integration with Bridgic...

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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:

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