Memory Allocator

Expert skill for custom memory allocator design optimized for language runtime needs

509 stars

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

$curl -o ~/.claude/skills/memory-allocator/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/programming-languages/skills/memory-allocator/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/memory-allocator/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Memory Allocator Compares

Feature / AgentMemory AllocatorStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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