Agent skill

Gpui Performance

by StudentWeis in StudentWeis/ropy

Performance optimization techniques for GPUI including rendering optimization, layout performance, memory management, and profiling strategies.

MITAuto-check passedDevelopment

Install Gpui Performance

skills CLI
$ npx skills add StudentWeis/ropy --skill gpui-performance -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install StudentWeis/ropy gpui-performance --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/StudentWeis/ropy.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/gpui-performance .claude/skills/gpui-performance && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gpui-performance
GitHub stars
194
Token cost
~3.6k tokens
SKILL.md length
281 words
Files
1
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

Performance optimization techniques for GPUI including rendering optimization, layout performance, memory management, and profiling strategies.

  • Works in 10 steps: Measure First: Profile before optimizing → Minimize Renders: Only cx.notify() when… → Cache Results: Memoize expensive… → …
  • User needs to optimize GPUI application performance
  • SKILL.md covers Metadata, Instructions and Resources
  • Calls cargo

What it does

Gpui Performance is an agent skill from StudentWeis/ropy. Performance optimization techniques for GPUI including rendering optimization, layout performance, memory management, and profiling strategies. Use when user needs to optimize GPUI application performance or debug performance issues.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Performance optimization. The repository describes itself as: Cross-platform, lightweight clipboard manager written in Rust and GPUI. The licence is MIT.

When your agent uses it

  • User needs to optimize GPUI application performance
  • Debug performance issues

Example prompts

  • “/gpui-performance”

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  1. Measure First: Profile before optimizing
  2. Minimize Renders: Only cx.notify() when necessary
  3. Cache Results: Memoize expensive computations
  4. Batch Updates: Group state changes
  5. Virtual Scrolling: For long lists
  6. Flat Layouts: Avoid deep nesting
  7. Fixed Sizing: When possible
  8. Monitor Memory: Watch for leaks
  9. Async Loading: Don't block UI
  10. Test Performance: Include benchmarks

What it can do on your machine

Read from SKILL.md and the folder at commit 4e784df. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • cargo

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gpui Performance loads about 3.6k tokens when it runs. Until then it costs about 63 tokens; SKILL.md has 281 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~63
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from StudentWeis/ropy at commit 4e784df, republished under its MIT licence (© StudentWeis). 281 words, ~3,649 tokens.

Download SKILL.mdSave it as .claude/skills/gpui-performance/SKILL.md (or your agent's skills folder).
name
gpui-performance
description
Performance optimization techniques for GPUI including rendering optimization, layout performance, memory management, and profiling strategies. Use when user needs to optimize GPUI application performance or debug performance issues.

GPUI Performance Optimization

Metadata

This skill provides comprehensive guidance on optimizing GPUI applications for rendering performance, memory efficiency, and overall runtime speed.

Instructions

Rendering Optimization
Understanding the Render Cycle
State Change → cx.notify() → Render → Layout → Paint → Display

Key Points:

  • Only call cx.notify() when state actually changes
  • Minimize work in render() method
  • Cache expensive computations
  • Reduce element count and nesting
Avoiding Unnecessary Renders
rust
// BAD: Renders on every frame
impl MyComponent {
    fn start_animation(&mut self, cx: &mut ViewContext<Self>) {
        cx.spawn(|this, mut cx| async move {
            loop {
                cx.update(|_, cx| cx.notify()).ok();  // Forces rerender!
                Timer::after(Duration::from_millis(16)).await;
            }
        }).detach();
    }
}

// GOOD: Only render when state changes
impl MyComponent {
    fn update_value(&mut self, new_value: i32, cx: &mut ViewContext<Self>) {
        if self.value != new_value {
            self.value = new_value;
            cx.notify();  // Only notify on actual change
        }
    }
}
Optimize Subscription Updates
rust
// BAD: Always rerenders on model change
let _subscription = cx.observe(&model, |_, _, cx| {
    cx.notify();  // Rerenders even if nothing relevant changed
});

// GOOD: Selective updates
let _subscription = cx.observe(&model, |this, model, cx| {
    let data = model.read(cx);

    // Only rerender if relevant field changed
    if data.relevant_field != this.cached_field {
        this.cached_field = data.relevant_field.clone();
        cx.notify();
    }
});
Memoization Pattern
rust
use std::cell::RefCell;
use std::collections::hash_map::DefaultHasher;
use std::hash::{Hash, Hasher};

struct MemoizedComponent {
    model: Model<Data>,
    cached_result: RefCell<Option<(u64, String)>>,  // (hash, result)
}

impl MemoizedComponent {
    fn expensive_computation(&self, cx: &ViewContext<Self>) -> String {
        let data = self.model.read(cx);

        // Calculate hash of input
        let mut hasher = DefaultHasher::new();
        data.relevant_fields.hash(&mut hasher);
        let hash = hasher.finish();

        // Return cached if unchanged
        if let Some((cached_hash, cached_result)) = &*self.cached_result.borrow() {
            if *cached_hash == hash {
                return cached_result.clone();
            }
        }

        // Compute and cache
        let result = perform_expensive_computation(&data);
        *self.cached_result.borrow_mut() = Some((hash, result.clone()));
        result
    }
}
Layout Performance
Minimize Layout Complexity
rust
// BAD: Deep nesting
div()
    .flex()
    .child(
        div()
            .flex()
            .child(
                div()
                    .flex()
                    .child(
                        div().child("Content")
                    )
            )
    )

// GOOD: Flat structure
div()
    .flex()
    .flex_col()
    .gap_4()
    .child("Header")
    .child("Content")
    .child("Footer")
Use Fixed Sizing When Possible
rust
// BETTER: Fixed sizes (no layout calculation)
div()
    .w(px(200.))
    .h(px(100.))
    .child("Fixed size")

// SLOWER: Dynamic sizing (requires layout calculation)
div()
    .w_full()
    .h_full()
    .child("Dynamic size")
Avoid Layout Thrashing
rust
// BAD: Reading layout during render
impl Render for BadComponent {
    fn render(&mut self, cx: &mut ViewContext<Self>) -> impl IntoElement {
        let width = cx.window_bounds().get_bounds().size.width;
        // Using width immediately causes layout thrashing
        div().w(width)
    }
}

// GOOD: Cache layout-dependent values
struct GoodComponent {
    cached_width: Pixels,
}

impl GoodComponent {
    fn on_window_resize(&mut self, cx: &mut ViewContext<Self>) {
        let width = cx.window_bounds().get_bounds().size.width;
        if self.cached_width != width {
            self.cached_width = width;
            cx.notify();
        }
    }
}
Virtual Scrolling for Long Lists
rust
struct VirtualList {
    items: Vec<String>,
    scroll_offset: f32,
    viewport_height: f32,
    item_height: f32,
}

impl Render for VirtualList {
    fn render(&mut self, cx: &mut ViewContext<Self>) -> impl IntoElement {
        // Calculate visible range
        let start_index = (self.scroll_offset / self.item_height).floor() as usize;
        let visible_count = (self.viewport_height / self.item_height).ceil() as usize;
        let end_index = (start_index + visible_count).min(self.items.len());

        // Only render visible items
        div()
            .h(px(self.viewport_height))
            .overflow_y_scroll()
            .on_scroll(cx.listener(|this, event, cx| {
                this.scroll_offset = event.scroll_offset.y;
                cx.notify();
            }))
            .child(
                div()
                    .h(px(self.items.len() as f32 * self.item_height))
                    .child(
                        div()
                            .absolute()
                            .top(px(start_index as f32 * self.item_height))
                            .children(
                                self.items[start_index..end_index]
                                    .iter()
                                    .map(|item| {
                                        div()
                                            .h(px(self.item_height))
                                            .child(item.as_str())
                                    })
                            )
                    )
            )
    }
}
Memory Management
Preventing Memory Leaks
rust
// LEAK: Subscription not stored
impl BadView {
    fn new(model: Model<Data>, cx: &mut ViewContext<Self>) -> Self {
        cx.observe(&model, |_, _, cx| cx.notify());  // Leak!
        Self { model }
    }
}

// CORRECT: Store subscription
struct GoodView {
    model: Model<Data>,
    _subscription: Subscription,  // Cleaned up on Drop
}

impl GoodView {
    fn new(model: Model<Data>, cx: &mut ViewContext<Self>) -> Self {
        let _subscription = cx.observe(&model, |_, _, cx| cx.notify());
        Self { model, _subscription }
    }
}
Avoid Circular References
rust
// BAD: Circular reference
struct CircularRef {
    self_view: Option<View<Self>>,  // Circular!
}

// GOOD: Use weak references or redesign
struct NoCycle {
    other_view: View<OtherView>,  // No cycle
}
Bounded Collections
rust
use std::collections::VecDeque;

const MAX_HISTORY: usize = 100;

struct BoundedHistory {
    items: VecDeque<Item>,
}

impl BoundedHistory {
    fn add_item(&mut self, item: Item) {
        self.items.push_back(item);

        // Maintain size limit
        while self.items.len() > MAX_HISTORY {
            self.items.pop_front();
        }
    }
}
Reuse Allocations
rust
struct BufferedComponent {
    buffer: String,  // Reused across operations
}

impl BufferedComponent {
    fn format_data(&mut self, data: &[Item]) -> &str {
        self.buffer.clear();  // Reuse allocation

        for item in data {
            use std::fmt::Write;
            write!(&mut self.buffer, "{}\n", item.name).ok();
        }

        &self.buffer
    }
}
Profiling Strategies
CPU Profiling with cargo-flamegraph
bash
# Install
cargo install flamegraph

# Profile application
cargo flamegraph --bin your-app

# With specific features
cargo flamegraph --bin your-app --features profiling

# Opens flamegraph.svg showing CPU time distribution
Memory Profiling
bash
# valgrind (Linux)
valgrind --tool=massif --massif-out-file=massif.out ./target/release/your-app
ms_print massif.out

# heaptrack (Linux)
heaptrack ./target/release/your-app
heaptrack_gui heaptrack.your-app.*.gz

# Instruments (macOS)
instruments -t "Allocations" ./target/release/your-app
Custom Performance Monitoring
rust
use std::time::Instant;

struct PerformanceMonitor {
    frame_times: VecDeque<Duration>,
    max_samples: usize,
}

impl PerformanceMonitor {
    fn new() -> Self {
        Self {
            frame_times: VecDeque::with_capacity(100),
            max_samples: 100,
        }
    }

    fn record_frame(&mut self, duration: Duration) {
        self.frame_times.push_back(duration);

        if self.frame_times.len() > self.max_samples {
            self.frame_times.pop_front();
        }

        // Warn if frame is slow (> 16ms for 60fps)
        if duration.as_millis() > 16 {
            eprintln!("⚠️  Slow frame: {}ms", duration.as_millis());
        }
    }

    fn average_fps(&self) -> f64 {
        if self.frame_times.is_empty() {
            return 0.0;
        }

        let total: Duration = self.frame_times.iter().sum();
        let avg = total / self.frame_times.len() as u32;
        1000.0 / avg.as_millis() as f64
    }

    fn percentile(&self, p: f64) -> Duration {
        let mut sorted: Vec<_> = self.frame_times.iter().copied().collect();
        sorted.sort();

        let index = (sorted.len() as f64 * p) as usize;
        sorted[index.min(sorted.len() - 1)]
    }
}

// Usage in component
impl MyView {
    fn measure_render<F>(&mut self, f: F, cx: &mut ViewContext<Self>)
    where
        F: FnOnce(&mut Self, &mut ViewContext<Self>)
    {
        let start = Instant::now();
        f(self, cx);
        let elapsed = start.elapsed();

        self.perf_monitor.record_frame(elapsed);

        // Log stats periodically
        if self.frame_count % 60 == 0 {
            println!(
                "Avg FPS: {:.1}, p95: {}ms, p99: {}ms",
                self.perf_monitor.average_fps(),
                self.perf_monitor.percentile(0.95).as_millis(),
                self.perf_monitor.percentile(0.99).as_millis(),
            );
        }
    }
}
Benchmark with Criterion
rust
// benches/component_bench.rs
use criterion::{black_box, criterion_group, criterion_main, Criterion, BenchmarkId};

fn render_benchmark(c: &mut Criterion) {
    let mut group = c.benchmark_group("rendering");

    for size in [10, 100, 1000].iter() {
        group.bench_with_input(
            BenchmarkId::from_parameter(size),
            size,
            |b, &size| {
                b.iter(|| {
                    App::test(|cx| {
                        let items = vec![Item::default(); size];
                        let view = cx.new_view(|cx| {
                            ListView::new(items, cx)
                        });

                        view.update(cx, |view, cx| {
                            black_box(view.render(cx));
                        });
                    });
                });
            }
        );
    }

    group.finish();
}

criterion_group!(benches, render_benchmark);
criterion_main!(benches);
Batching Updates
rust
// BAD: Multiple individual updates
for item in items {
    self.model.update(cx, |model, cx| {
        model.add_item(item);  // Triggers rerender each time!
        cx.notify();
    });
}

// GOOD: Batch into single update
self.model.update(cx, |model, cx| {
    for item in items {
        model.add_item(item);
    }
    cx.notify();  // Single rerender
});
Async Rendering Optimization
rust
struct AsyncView {
    loading_state: Model<LoadingState>,
}

impl AsyncView {
    fn load_data(&mut self, cx: &mut ViewContext<Self>) {
        let loading_state = self.loading_state.clone();

        // Show loading immediately
        self.loading_state.update(cx, |state, cx| {
            *state = LoadingState::Loading;
            cx.notify();
        });

        // Load asynchronously
        cx.spawn(|_, mut cx| async move {
            // Fetch data
            let data = fetch_data().await?;

            // Update state once
            cx.update_model(&loading_state, |state, cx| {
                *state = LoadingState::Loaded(data);
                cx.notify();
            })?;

            Ok::<_, anyhow::Error>(())
        }).detach();
    }
}
Caching Strategies
Result Caching
rust
use std::collections::HashMap;

struct CachedRenderer {
    cache: RefCell<HashMap<String, CachedElement>>,
}

impl CachedRenderer {
    fn render_cached(
        &self,
        key: String,
        render_fn: impl FnOnce() -> AnyElement,
    ) -> AnyElement {
        let mut cache = self.cache.borrow_mut();

        cache.entry(key)
            .or_insert_with(|| CachedElement::new(render_fn()))
            .element
            .clone()
    }

    fn invalidate(&self, key: &str) {
        self.cache.borrow_mut().remove(key);
    }
}

Resources

Performance Targets

Rendering:

  • Target: 60 FPS (16.67ms per frame)
  • Render + Layout: ~10ms
  • Paint: ~6ms
  • Warning: Any frame > 16ms

Memory:

  • Monitor heap growth
  • Warning: Steady increase (leak)
  • Target: Stable after initialization

Startup:

  • Window display: < 100ms
  • Fully interactive: < 500ms
Profiling Tools

CPU Profiling:

  • cargo-flamegraph: Visualize CPU time
  • perf (Linux): System-level profiling
  • Instruments (macOS): Apple's profiler

Memory Profiling:

  • valgrind/massif: Memory usage tracking
  • heaptrack: Heap allocation tracking
  • Instruments: Memory allocations

Benchmarking:

  • criterion: Statistical benchmarking
  • cargo bench: Built-in benchmarks
  • hyperfine: Command-line tool benchmarking
Best Practices
  1. Measure First: Profile before optimizing
  2. Minimize Renders: Only cx.notify() when necessary
  3. Cache Results: Memoize expensive computations
  4. Batch Updates: Group state changes
  5. Virtual Scrolling: For long lists
  6. Flat Layouts: Avoid deep nesting
  7. Fixed Sizing: When possible
  8. Monitor Memory: Watch for leaks
  9. Async Loading: Don't block UI
  10. Test Performance: Include benchmarks
Common Bottlenecks
  • Subscription in render (memory leak)
  • Expensive computation in render
  • Deep component nesting
  • Unnecessary rerenders
  • Layout thrashing
  • Large lists without virtualization
  • Memory leaks from circular refs
  • Unbounded collections

© StudentWeis, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/gpui-performance of StudentWeis/ropy.

Open the folder on GitHubat commit 4e784df

Compare with similar skills

Gpui Performance next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Gpui Performance compared with similar skills
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Pycrazyguitar/pysheeet8.2k—~886Automated safety check: PassMIT
Cmux Debugging Guidemanaflow-ai/cmux28k1 repos~1.1kAutomated safety check: PassCustom licence
Electron Heap Snapshot Analysiskeybase/client9.3k—~875Automated safety check: PassBSD-3-Clause

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Categories

Questions about Gpui Performance

What does Gpui Performance do?

Performance optimization techniques for GPUI including rendering optimization, layout performance, memory management, and profiling strategies. Gpui Performance is an agent skill from StudentWeis/ropy. Performance optimization techniques for GPUI including rendering optimization, layout performance, memory management, and profiling strategies.

When should I use Gpui Performance?

Gpui Performance fits situations like: user needs to optimize GPUI application performance; debug performance issues.

How do I install Gpui Performance in Claude Code?

Run `npx skills add StudentWeis/ropy --skill gpui-performance -a claude-code`. Or copy the skill folder (.agents/skills/gpui-performance in StudentWeis/ropy) into .claude/skills/gpui-performance in your project. Claude Code loads it when a task matches its description.

How do I install Gpui Performance in Codex?

Run `npx skills add StudentWeis/ropy --skill gpui-performance -a codex`. Or copy the skill folder (.agents/skills/gpui-performance in StudentWeis/ropy) into .agents/skills/gpui-performance in your project. Codex loads it when a task matches its description.

Can I use Gpui Performance in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add StudentWeis/ropy --skill gpui-performance -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gpui-performance, .gemini/skills/gpui-performance, .github/skills/gpui-performance and .opencode/skills/gpui-performance in your project.

What does Gpui Performance need to run?

Going by SKILL.md and its folder, Gpui Performance needs the command-line tools its instructions call (cargo).

Does Gpui Performance access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Gpui Performance safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gpui Performance use?

Gpui Performance is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gpui Performance use?

About 3.6k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Gpui Performance?

Skills that share tags, products or a category with Gpui Performance: Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Py (crazyguitar/pysheeet, 8.2k stars) and Cmux Debugging Guide (manaflow-ai/cmux, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gpui Performance?

StudentWeis (a GitHub user) maintains it in StudentWeis/ropy, which has 194 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on September 9, 2026.

Source: StudentWeis/ropy on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.