profiling-memory-allocations

Identify and analyze memory allocations in Truffle-based guest-language code.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/antonykamp/cc-truffle-performance-plugin --skill profiling-memory-allocations
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: profiling-memory-allocations
Source: https://github.com/antonykamp/cc-truffle-performance-plugin/tree/main/skills/profiling-memory-allocations
Command: npx skills add https://github.com/antonykamp/cc-truffle-performance-plugin --skill profiling-memory-allocations

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Memory allocations in guest-language code can obscure performance bottlenecks and GC pressure; this skill helps identify allocation hotspots and unnecessary object creation to optimize runtime behavior.

Core Features & Use Cases

  • Track allocation sites and object types to locate hot paths.
  • Measure memory pressure and GC impact for iterative optimizations.
  • Detect opportunities for escape analysis and object reuse in Truffle-based runtimes.

Quick Start

Launch the memory allocator tracer for your program and review the resulting allocation report.

Frequently Asked Questions about profiling-memory-allocations

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I profile memory allocations to identify hot spots in Truffle-based guest-language code?▼

To profile memory allocations, launch the memory tracing runtime to analyze hot loops and high-allocation paths. It reports allocation sites with object types and counts, revealing hotspots and unnecessary object creation in guest-language code.

What is the best way to measure GC pressure and memory allocation impact during benchmarking?▼

Measuring GC pressure requires tracking allocation sites and object types in GC-sensitive regions. The skill identifies object creation hotspots and reports allocation counts to support iterative runtime optimizations and measure memory impact.

Can I use allocation profiling to detect opportunities for escape analysis and object reuse?▼

Yes, allocation profiling detects opportunities for escape analysis and object reuse in Truffle-based runtimes. By analyzing hot paths and high-allocation regions, it identifies unnecessary object creation that can be optimized.

Does this memory allocation tracer support exporting stack traces and JSON output for downstream tooling?▼

The memory allocation tracer supports optional stack traces and can output JSON for downstream tooling. It requires a memory tracing runtime to report allocation sites, counts, and object types across guest-language code.

When do I need to trace memory allocations in guest-language code?▼

You need to trace memory allocations when optimizing hot loops, high-allocation paths, and GC-sensitive regions during development and benchmarking. It reveals performance bottlenecks and memory pressure caused by unnecessary object creation.