Code Review Checklist
shareAI-lab/learn-claude-code
Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
Performance optimization techniques for GPUI including rendering optimization, layout performance, memory management, and profiling strategies.
$ npx skills add StudentWeis/ropy --skill gpui-performance -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install StudentWeis/ropy gpui-performance --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "gpui-performance" agent skill from https://github.com/StudentWeis/ropy/tree/main/.agents/skills/gpui-performance into .claude/skills/gpui-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpui-performance", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/StudentWeis/ropy/tree/main/.agents/skills/gpui-performanceType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add StudentWeis/ropy --skill gpui-performance -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install StudentWeis/ropy gpui-performance --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/StudentWeis/ropy.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/gpui-performance .agents/skills/gpui-performance && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gpui-performance" agent skill from https://github.com/StudentWeis/ropy/tree/main/.agents/skills/gpui-performance into .agents/skills/gpui-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpui-performance", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add StudentWeis/ropy --skill gpui-performance -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install StudentWeis/ropy gpui-performance --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/StudentWeis/ropy.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/gpui-performance .cursor/skills/gpui-performance && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gpui-performance" agent skill from https://github.com/StudentWeis/ropy/tree/main/.agents/skills/gpui-performance into .cursor/skills/gpui-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpui-performance", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/StudentWeis/ropy.git --path .agents/skills/gpui-performance--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add StudentWeis/ropy --skill gpui-performance -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install StudentWeis/ropy gpui-performance --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/StudentWeis/ropy.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/gpui-performance .gemini/skills/gpui-performance && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gpui-performance" agent skill from https://github.com/StudentWeis/ropy/tree/main/.agents/skills/gpui-performance into .gemini/skills/gpui-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpui-performance", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install StudentWeis/ropy gpui-performanceInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add StudentWeis/ropy --skill gpui-performance -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/StudentWeis/ropy.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/gpui-performance .github/skills/gpui-performance && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gpui-performance" agent skill from https://github.com/StudentWeis/ropy/tree/main/.agents/skills/gpui-performance into .github/skills/gpui-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpui-performance", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add StudentWeis/ropy --skill gpui-performance -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install StudentWeis/ropy gpui-performance --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/StudentWeis/ropy.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/gpui-performance .opencode/skills/gpui-performance && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gpui-performance" agent skill from https://github.com/StudentWeis/ropy/tree/main/.agents/skills/gpui-performance into .opencode/skills/gpui-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpui-performance", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
gpui-performancePerformance 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. 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.
10 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 4e784df. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
cargoFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from StudentWeis/ropy at commit 4e784df, republished under its MIT licence (© StudentWeis). 281 words, ~3,649 tokens.
.claude/skills/gpui-performance/SKILL.md (or your agent's skills folder).This skill provides comprehensive guidance on optimizing GPUI applications for rendering performance, memory efficiency, and overall runtime speed.
State Change → cx.notify() → Render → Layout → Paint → DisplayKey Points:
cx.notify() when state actually changesrender() method// 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
}
}
}// 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();
}
});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
}
}// 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")// 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")// 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();
}
}
}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())
})
)
)
)
}
}// 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 }
}
}// 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
}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();
}
}
}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
}
}# 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# 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-appuse 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(),
);
}
}
}// 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);// 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
});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();
}
}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);
}
}Rendering:
Memory:
Startup:
CPU Profiling:
Memory Profiling:
Benchmarking:
cx.notify() when necessary© StudentWeis, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/gpui-performance of StudentWeis/ropy.
Open the folder on GitHubat commit 4e784df
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Gpui Performance this skillStudentWeis/ropy | 194 | — | ~3.6k | Automated safety check: Pass | MIT | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 5 repos | ~1.1k | Automated safety check: Pass | MIT | |
| LLM Torch Profiler Analysissgl-project/sglang | 37k | 2 repos | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Pycrazyguitar/pysheeet | 8.2k | — | ~886 | Automated safety check: Pass | MIT | |
| Cmux Debugging Guidemanaflow-ai/cmux | 28k | 1 repos | ~1.1k | Automated safety check: Pass | Custom licence | |
| Electron Heap Snapshot Analysiskeybase/client | 9.3k | — | ~875 | Automated safety check: Pass | BSD-3-Clause |
shareAI-lab/learn-claude-code
Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
crazyguitar/pysheeet
Comprehensive Python programming reference covering syntax, concurrency, networking, databases, ML/LLM development, and HPC.
manaflow-ai/cmux
Covers debug logging, the Debug menu, profiling rules and runtime pitfalls for working on the cmux macOS terminal app.
keybase/client
Analyzes V8, Chrome and Electron .heapsnapshot files with Node scripts to find memory leaks, detached DOM nodes and the retainer paths that keep objects alive.
ben-manes/caffeine
Runs controlled JMH experiments on the Caffeine cache to find shared contention and hot-path waste, then reviews correctness and returns a reviewable patch.
StudentWeis/ropy
Implementing custom elements using GPUI's low-level Element API (vs.
StudentWeis/ropy
Entity management and state handling in GPUI. An agent skill from StudentWeis/ropy.
StudentWeis/ropy
Review a code change, diff, pull request, module, or test suite for code quality, comment and documentation quality, and test quality.
StudentWeis/ropy
Repository contribution workflow. An agent skill from StudentWeis/ropy.
StudentWeis/ropy
Action definitions and keyboard shortcuts in GPUI. An agent skill from StudentWeis/ropy.
StudentWeis/ropy
Async operations and background tasks in GPUI. An agent skill from StudentWeis/ropy.
Categories
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.
Gpui Performance fits situations like: user needs to optimize GPUI application performance; debug performance issues.
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.
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.
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.
Going by SKILL.md and its folder, Gpui Performance needs the command-line tools its instructions call (cargo).
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.
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.
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.
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.
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.
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.