Karpathy Guidelines
Applies Andrej Karpathy's principles for effective coding & project management to improve output quality.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add karpathy-guidelines npx -- -y @trustedskills/karpathy-guidelines
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"karpathy-guidelines": {
"command": "npx",
"args": [
"-y",
"@trustedskills/karpathy-guidelines"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The karpathy-guidelines skill provides practical, hands-on advice and best practices for training neural networks, particularly in the context of deep learning and AI development. It draws from Andrej Karpathy’s expertise, offering insights into model architecture, optimization techniques, and debugging strategies.
When to use it
- You're designing a new neural network and need guidance on layer configurations or hyperparameter tuning.
- You're encountering training instability or poor convergence in your deep learning models.
- You want to improve the efficiency of your AI agent's learning process through optimized training routines.
Key capabilities
- Practical advice for debugging neural networks
- Insights into model architecture design
- Optimization techniques for training efficiency
Example prompts
- "How can I stabilize my neural network’s training?"
- "What are some best practices for designing a deep learning model?"
- "Can you explain how to debug exploding gradients in my AI agent's model?"
Tips & gotchas
- This skill is most effective when used alongside specific technical problems or model architectures.
- While it provides general guidance, results may vary depending on the exact use case and data being used.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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Passed automated security scans.