Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
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Critical
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Security
2 findings: 1 critical severity, 1 high severity. Installing this skill is not recommended: please review these findings carefully if you do intend to do so.
Detected a suspicious URL in the skill instructions that could lead the agent to download and execute malicious scripts or binaries. This includes links to executables from untrusted sources, typosquatting of official packages, URL shorteners that obscure the destination, and personal file hosting services.
The list includes an explicit malicious domain (https://malicious.com) referenced in the skill, which is a clear suspicious download/source indicator and can be used to distribute malware.
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
The skill contains explicit examples that embed plaintext credentials into commands (e.g., agent-browser fill "password123" and echo "pass" | agent-browser ...), which would force an LLM to output secret values verbatim if it follows those examples.
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
In SKILL.md and the provided templates (e.g., templates/capture-workflow.sh), the runtime workflow uses agent-browser to open a user-supplied target URL and then ingests first-party page text/DOM accessibility output via `snapshot -i` and `get text body`, so outsider-authored free text from that page can flow into the LLM context.
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