agent-eyes
Visual context analyzer for AI agents. Provides screenshots, accessibility scans, DOM snapshots, and element descriptions for web pages. Use when you need to see what a web page looks like, analyze accessibility issues, inspect DOM structure, or get detailed element information. Triggers on requests like "take a screenshot", "check accessibility", "what does this page look like", "analyze the UI", "inspect this element", or any visual/UI analysis task.
npx skills add majiayu000/claude-skill-registry --skill agent-eyes-edrouhardmicrosoft-agent-canvas-skills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Agent Eyes Visual context analyzer for web pages. Provides AI agents with the ability to "see" web applications through screenshots, accessibility scans, DOM snapshots, and element descriptions. ## Prerequisites - Python 3.10+ - `uv` package manager (recommended) - Playwright browsers installed: `playwright install chromium` ## Compact Mode (Token-Efficient Output) **All commands support `--compact` / `-c` flag** for token-efficient output: | Mode | Screenshot | DOM | A11y | Total Tokens | |------|------------|-----|------|--------------| | Standard | Base64 inline | depth=5, 20 children | Full violations | ~500K+ | | **Compact** | File path only | depth=3, 10 children | Summary only | **~3-5K** | Use compact mode when context window size is a concern (which is most of the time). ```bash # Compact context - reduces ~500K tokens to ~3-5K tokens uv run $SKILL_DIR/agent_eyes.py context http://localhost:3000 --compact # Compact screenshot - always saves to file, never returns base64 uv run $SKILL_DIR/agent_eyes.py screenshot http://localhost:3000 --compact # Compact a11y - returns summary + top N issues only uv run $SKILL_DIR/agent_eyes.py a11y http://localhost:3000 --compact # Compa
- Prerequisites
- Compact Mode (Token-Efficient Output)
- Commands
- Screenshot
- Accessibility Scan
- DOM Snapshot
- Describe Element
- Full Context
- Output Format
- Compact Mode Output Examples
- Typical Agent Workflow
- Example: Analyze and Fix A11y Issues
- Notes
- Token Budget Guide
Compact context - reduces ~500K tokens to ~3-5K tokens uv run $SKILL_DIR/agent_eyes.py context http://localhost:3000 --compact Compact screenshot - always saves to file, never returns base64 uv run $SKILL_DIR/agent_eyes.py screenshot http://localhost:3000 --compact Compact a11y - returns summary + top N issues only uv run $SKILL_DIR/agent_eyes.py a11y http://localhost:3000 --compact Compact DOM - stricter limits on depth and children uv run $SKILL_DIR/agent_eyes.py dom http://localhost:3000 --compact Full page screenshot (saves to .canvas/screenshots/) uv run $SKILL_DIR/agent_eyes.py screenshot http://localhost:3000
What does the agent-eyes skill do?
Visual context analyzer for AI agents. Provides screenshots, accessibility scans, DOM snapshots, and element descriptions for web pages. Use when you need to see what a web page looks like, analyze accessibility issues, inspect DOM structure, or get detailed element information. Triggers on requests like "take a screenshot", "check accessibility", "what does this page look like", "analyze the UI", "inspect this element", or any visual/UI analysis task.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill agent-eyes-edrouhardmicrosoft-agent-canvas-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.