Memory Allocator
Expert skill for custom memory allocator design optimized for language runtime needs
Best use case
Memory Allocator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Expert skill for custom memory allocator design optimized for language runtime needs
Teams using Memory Allocator 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/memory-allocator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Memory Allocator Compares
| Feature / Agent | Memory Allocator | 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?
Expert skill for custom memory allocator design optimized for language runtime needs
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
# Memory Allocator Skill
## Overview
Expert skill for custom memory allocator design optimized for language runtime needs.
## Capabilities
- Implement bump/arena allocators
- Implement free-list allocators with size classes
- Design slab allocators for fixed-size objects
- Implement thread-local allocation buffers (TLAB)
- Handle large object allocation strategies
- Implement memory pooling and recycling
- Design memory profiling and statistics
- Implement address space layout optimization
## Target Processes
- memory-allocator-design.js
- garbage-collector-implementation.js
- interpreter-implementation.js
- bytecode-vm-implementation.js
## Dependencies
jemalloc, tcmalloc references
## Usage Guidelines
1. **Size Classes**: Design size classes to minimize internal fragmentation
2. **Thread Safety**: Use thread-local allocation for hot paths
3. **Large Objects**: Handle large objects separately from small allocations
4. **Profiling**: Build allocation statistics from the start
5. **GC Integration**: Design allocator API with GC integration in mind
## Output Schema
```json
{
"type": "object",
"properties": {
"allocatorType": {
"type": "string",
"enum": ["bump", "free-list", "slab", "hybrid"]
},
"sizeClasses": {
"type": "array",
"items": { "type": "integer" }
},
"threadSafety": {
"type": "string",
"enum": ["single-threaded", "tlab", "lock-free"]
},
"generatedFiles": {
"type": "array",
"items": { "type": "string" }
}
}
}
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