Visual Debugger
This Visual Debugger allows you to step through code execution visually, pinpointing issues and understanding program flow more effectively.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add visual-debugger npx -- -y @trustedskills/visual-debugger
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"visual-debugger": {
"command": "npx",
"args": [
"-y",
"@trustedskills/visual-debugger"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The visual-debugger skill allows you to examine and understand the internal state of AI agents, providing insights into their reasoning process. It presents agent outputs in a structured, visual format, enabling easier debugging and refinement. This helps identify unexpected behavior or biases within the agent's logic.
When to use it
- Debugging complex workflows: When an agent is producing incorrect results in a multi-step task, visualize its internal state at each step to pinpoint the error.
- Understanding reasoning chains: Examine how an agent arrives at conclusions by visualizing intermediate steps and data transformations.
- Identifying biases: Inspecting the visual representation of agent decisions can reveal patterns or shortcuts that lead to biased outcomes.
- Improving prompt engineering: Use the debugging information to refine prompts and guide the agent towards more desirable behavior.
Key capabilities
- Visualizes agent internal state
- Structured output presentation
- Debugging assistance
- Reasoning chain analysis
Example prompts
- "Debug my agent's response to 'Summarize this article.'"
- "Show me the intermediate steps of the agent when calculating the shipping cost."
- "Visualize how the agent is classifying these images."
Tips & gotchas
The effectiveness of visual debugging depends on the complexity of the underlying AI agent and the richness of its internal state representation. It may require some experimentation to interpret the visualizations correctly.
Tags
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