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rust-performance-analyzer

Testing & Quality
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Analyzes Rust code for performance bottlenecks, memory inefficiencies, and optimization opportunities. Use when discussing performance, slow code, memory usage, profiling, benchmarks, or optimization.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/rust-performance-analyzer/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/rust-performance-analyzer/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

Rust Performance Analyzer

Expert skill for analyzing and optimizing Rust application performance.

When to Use

Activate this skill when the user:

  • Mentions "slow", "performance", "optimize", "bottleneck"
  • Asks about memory usage or allocation patterns
  • Wants to profile or benchmark code
  • Reports laggy UI or frame drops
  • Discusses cache efficiency or data layout

Analysis Process

1. Identify Hot Paths

Look for:

  • Frequent allocations (Vec::new(), String::new() in loops)
  • Unnecessary cloning (.clone() where borrow would work)
  • Hash map lookups in tight loops
  • Box/Arc indirection overhead

2. Memory Layout Analysis

Check:

  • Struct field ordering (largest first for alignment)
  • Use of #[repr(C)] where needed
  • Option niche optimization usage
  • Cache line friendliness

3. Concurrency Patterns

Evaluate:

  • Lock contention (Mutex, RwLock usage)
  • parking_lot vs std sync primitives
  • Atomic operations appropriateness
  • Send/Sync bounds efficiency

4. FLUI-Specific Patterns

Focus on:

  • Signal update frequency
  • Rebuild triggering patterns
  • Layout phase efficiency
  • Paint layer caching

Optimization Techniques

Allocation Reduction

// Bad: Allocates on every call
fn process(items: &[Item]) -> Vec<ProcessedItem> {
    items.iter().map(|i| process_one(i)).collect()
}

// Good: Reuse buffer
fn process_into(items: &[Item], buffer: &mut Vec<ProcessedItem>) {
    buffer.clear();
    buffer.extend(items.iter().map(|i| process_one(i)));
}

Clone Elimination

// Bad: Unnecessary clone
let data = self.data.clone();
process(&data);

// Good: Borrow directly
process(&self.data);

Interior Mutability

// Use RefCell/Cell for single-threaded
// Use parking_lot::{Mutex, RwLock} for multi-threaded
// Use atomics for simple counters/flags

Profiling Commands

# Build with debug symbols
cargo build --release

# CPU profiling (requires cargo-flamegraph)
cargo flamegraph --example <name>

# Memory profiling
RUSTFLAGS="-Z sanitizer=address" cargo +nightly run --example <name>

# Benchmarking
cargo bench

Output Format

Provide:

  1. Identified Issues: List of performance problems found
  2. Impact Assessment: Severity (High/Medium/Low)
  3. Optimization Suggestions: Specific code changes
  4. Metrics: Before/after estimates where possible