Best use case
micro-optimizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Apply language-specific micro-optimizations
Teams using micro-optimizer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/micro-optimizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How micro-optimizer Compares
| Feature / Agent | micro-optimizer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Apply language-specific micro-optimizations
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Micro-Optimizer Skill
## Purpose
Apply language-specific micro-optimizations to squeeze maximum performance from competitive programming solutions.
## Capabilities
- C++ optimization tricks (fast I/O, pragma optimizations)
- Python optimization (PyPy hints, list comprehensions)
- Memory layout optimization
- Vectorization opportunities
- Compiler-specific optimizations
## Target Processes
- code-level-optimization
- io-optimization
- memory-optimization
## Optimization Catalog
### C++ Optimizations
- Fast I/O: `ios_base::sync_with_stdio(false)`
- Pragma optimizations: `#pragma GCC optimize`
- Inline expansion
- Loop unrolling
- Memory prefetching
### Python Optimizations
- Use PyPy when possible
- List comprehensions over loops
- Local variable caching
- `__slots__` for classes
- Avoiding global lookups
### General Optimizations
- Branch prediction hints
- Cache-friendly data layout
- Avoiding unnecessary copies
- Bit manipulation tricks
## Input Schema
```json
{
"type": "object",
"properties": {
"code": { "type": "string" },
"language": {
"type": "string",
"enum": ["cpp", "python", "java"]
},
"optimizationLevel": {
"type": "string",
"enum": ["safe", "aggressive", "maximum"]
},
"preserveReadability": { "type": "boolean", "default": true }
},
"required": ["code", "language"]
}
```
## Output Schema
```json
{
"type": "object",
"properties": {
"success": { "type": "boolean" },
"optimizedCode": { "type": "string" },
"appliedOptimizations": { "type": "array" },
"expectedSpeedup": { "type": "string" },
"warnings": { "type": "array" }
},
"required": ["success", "optimizedCode"]
}
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