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computer-use-agents

Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives.

49

Quality

55%

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SecuritybySnyk

High

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tessl review fix ./plugins/antigravity-awesome-skills/skills/computer-use-agents/SKILL.md
SKILL.md
Quality
Evals
Security
High

W007: Insecure credential handling detected in skill instructions.

What this means

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.

Why it was flagged

The skill requires the LLM to emit actionable JSON (e.g., {"type":"type","text":"..."}) and bash/tool commands that may include user credentials or tokens verbatim (for logins, typing, or API calls), which forces secrets to appear in model outputs and creates an exfiltration risk.

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Low

W011: Third-party content exposure detected (indirect prompt injection risk).

What this means

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.

Why it was flagged

The skill's BrowserUseAgent explicitly captures page snapshots (get_page_snapshot) and feeds page DOM/text/URL into the LLM in run_with_llm (with examples like "Go to weather.com"), and the vision-based agents also pass screenshots to the model, so arbitrary public webpages and user-generated web content are fetched and interpreted to drive actions—creating a clear vector for indirect prompt injection.

Repository
popey/claude-code-skills
Audited
Security analysis
Snyk

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