Llvm Optimization
Analyzes and suggests LLVM optimization passes to improve code performance based on profiling data.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add llvm-optimization npx -- -y @trustedskills/llvm-optimization
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"llvm-optimization": {
"command": "npx",
"args": [
"-y",
"@trustedskills/llvm-optimization"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill leverages LLVM's optimization passes to improve the performance of code. It can analyze and transform existing code, reducing execution time and resource consumption through techniques like loop unrolling, function inlining, and dead code elimination. The skill aims to automatically apply these optimizations without requiring manual intervention from a developer.
When to use it
- Performance bottlenecks: Identify slow functions or sections of code and optimize them for faster execution.
- Resource-constrained environments: Reduce memory usage and improve efficiency in embedded systems or mobile applications.
- Legacy code optimization: Improve the performance of older codebases without significant refactoring efforts.
- Compiler plugin development: Integrate optimizations into custom compilers or build processes.
Key capabilities
- Automatic code analysis
- Loop unrolling
- Function inlining
- Dead code elimination
- Performance profiling integration
Example prompts
- "Optimize this C++ function for speed: [code snippet]"
- "Can you reduce the memory footprint of this Rust program?"
- "Profile and optimize this Python script."
Tips & gotchas
The effectiveness of LLVM optimization depends heavily on the code's structure. Complex or highly dynamic code may not benefit as much from automated optimizations, requiring manual tuning for best results.
Tags
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