React Doctor
makeplane/plane
Scans React code for lint, accessibility, bundle size and architecture issues, reports a health score and checks that changes do not lower it.
Measure runtime perf, bundle size, and memory impact of unicode-segmenter changes.
Install Benchmark
$ npx skills add cometkim/unicode-segmenter --skill benchmark -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install cometkim/unicode-segmenter benchmark --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/cometkim/unicode-segmenter.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/benchmark .claude/skills/benchmark && 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 "benchmark" agent skill from https://github.com/cometkim/unicode-segmenter/tree/main/.claude/skills/benchmark into .claude/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", 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/cometkim/unicode-segmenter/tree/main/.claude/skills/benchmarkType 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 cometkim/unicode-segmenter --skill benchmark -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install cometkim/unicode-segmenter benchmark --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cometkim/unicode-segmenter.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/benchmark .agents/skills/benchmark && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "benchmark" agent skill from https://github.com/cometkim/unicode-segmenter/tree/main/.claude/skills/benchmark into .agents/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", 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 cometkim/unicode-segmenter --skill benchmark -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install cometkim/unicode-segmenter benchmark --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cometkim/unicode-segmenter.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/benchmark .cursor/skills/benchmark && 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 "benchmark" agent skill from https://github.com/cometkim/unicode-segmenter/tree/main/.claude/skills/benchmark into .cursor/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", 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/cometkim/unicode-segmenter.git --path .claude/skills/benchmark--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 cometkim/unicode-segmenter --skill benchmark -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install cometkim/unicode-segmenter benchmark --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cometkim/unicode-segmenter.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/benchmark .gemini/skills/benchmark && 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 "benchmark" agent skill from https://github.com/cometkim/unicode-segmenter/tree/main/.claude/skills/benchmark into .gemini/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", 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 cometkim/unicode-segmenter benchmarkInstalls 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 cometkim/unicode-segmenter --skill benchmark -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/cometkim/unicode-segmenter.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/benchmark .github/skills/benchmark && 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 "benchmark" agent skill from https://github.com/cometkim/unicode-segmenter/tree/main/.claude/skills/benchmark into .github/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", 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 cometkim/unicode-segmenter --skill benchmark -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install cometkim/unicode-segmenter benchmark --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cometkim/unicode-segmenter.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/benchmark .opencode/skills/benchmark && 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 "benchmark" agent skill from https://github.com/cometkim/unicode-segmenter/tree/main/.claude/skills/benchmark into .opencode/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", 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.
Facts
- Skill name
benchmark- GitHub stars
- 112
- Token cost
- ~1.3k tokens
- SKILL.md length
- 662 words
- Files
- 1
- Skills in repo
- 4
- Repo updated
- First seen
- Licence
- MIT
At a glance
Measure runtime perf, bundle size, and memory impact of unicode-segmenter changes.
- Works in 3 steps: Materialize the baseline's module… → Write one scratch mitata script… → Run at least twice with declaration…
- Evaluating any src/ change
- SKILL.md covers Commands, Interpreting results, A/B against a baseline revision and Size-sensitive coding notes…, plus 1 more section
- Calls yarn, node and bun
What it does
Benchmark is an agent skill from cometkim/unicode-segmenter. Measure runtime perf, bundle size, and memory impact of unicode-segmenter changes. Use when evaluating any src/ change, running yarn perf / bundle-stats / memory-stats, comparing against a baseline revision, or when benchmark numbers look noisy or contradictory.
Its SKILL.md is about 1.3k 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 Frontend & Design, covering Web performance. The repository describes itself as: A lightweight implementation of the Unicode Text Segmentation (UAX 29). The licence is MIT.
When your agent uses it
- Evaluating any src/ change
- Running yarn perf / bundle-stats / memory-stats
- Comparing against a baseline revision
- Benchmark numbers look noisy
Example prompts
- “/benchmark”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Materialize the baseline's module closure with read-only git into the scratchpad
- Write one scratch mitata script importing BOTH implementations plus _testcases.js, registering old/new for each case in the same process.
- Run at least twice with declaration order swapped to detect order bias.
What it can do on your machine
Read from SKILL.md and the folder at commit 5d3c738. 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:
yarnnodebungit
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use yarn and git, which can reach the network depending on how they are called.
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
Benchmark loads about 1.3k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 662 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.
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 cometkim/unicode-segmenter at commit 5d3c738, republished under its MIT licence (© cometkim). 662 words, ~1,290 tokens.
Download SKILL.mdSave it as
.claude/skills/benchmark/SKILL.md (or your agent's skills folder).- name
- benchmark
- description
- Measure runtime perf, bundle size, and memory impact of unicode-segmenter changes. Use when evaluating any src/ change, running yarn perf / bundle-stats / memory-stats, comparing against a baseline revision, or when benchmark numbers look noisy or contradictory.
Benchmarking unicode-segmenter
This library's priorities, in order: bundle size, runtime perf, memory footprint. Measure all three before claiming a change is a win — they regularly trade against each other.
Commands
| Axis | Command | Notes |
|---|---|---|
| Runtime perf (Node) | yarn perf:grapheme (also :general, :emoji) | mitata; script passes --expose-gc |
| Runtime perf (Bun) | bun --bun run perf:grapheme | --bun forces the Bun runtime instead of Node |
| Runtime perf (browser) | yarn perf:grapheme:browser | vite page; capture procedure in the bench-records skill |
| Runtime perf (Hermes / QuickJS) | yarn perf:grapheme:hermes, yarn perf:grapheme:quickjs | Metro-based; see Metro warning |
| Bundle size | yarn bundle-stats:grapheme | esbuild; minified + gzip + brotli per library |
| Hermes bytecode | yarn bundle-stats:grapheme:hermes | React Native proxy metric |
| Memory | yarn memory-stats:grapheme | retained heap per library; forked child per lib, median of 5 samples × 10 uses |
Benchmarks import src/*.js directly — no build step needed, current checkout state is what gets measured.
Bundle-stats env options: PRINT_FORMAT=markdown prints a README-ready markdown table instead of console.table; UPDATE_README=true rewrites the matching README comparison table in place (rows/columns matched by name ignoring * footnote marks, which are preserved; hand-maintained cells like Unicode®/ESM? and the Intl.Segmenter row are untouched).
Interpreting results
- Compare ratios within a run, not absolute ns across runs. Apple Silicon clock scaling (3.7–4.5 GHz observed on M4 Pro) swings absolute numbers ±20% between runs. Each perf run includes the competitors, so the
summary"x faster than" lines are the stable signal. - Take best-of-3 when absolute numbers matter; close heavy apps first.
- Do not add mitata's
.gc('inner')when comparing — it attributes per-iteration GC cost to the benchmark and distorts small cases. - For bundle size, gzip/brotli are what users pay. Raw or minified chars can move opposite to compressed size (the base36-VLQ data encoding beats base64 after gzip despite more raw characters).
- Per-case reporting: the six shared cases (ASCII, emoji/ZWJ, Hindi/InCB, Zalgo, tweet, code) rarely move in one direction. Report each; flag any case regressing >10%.
A/B against a baseline revision
Never move test inputs by copy-paste: they contain invisible codepoints (ZWJ, variation selectors) and were mangled exactly that way once. Always import benchmark/grapheme/_testcases.js.
- Materialize the baseline's module closure with read-only git into the scratchpad:
git show origin/main:src/grapheme.js > <scratch>/baseline/grapheme.js— repeat forcore.js,_grapheme_data.js, and whatever else that revision imports. - Write one scratch mitata script importing BOTH implementations plus
_testcases.js, registering old/new for each case in the same process. - Run at least twice with declaration order swapped to detect order bias.
Show full SKILL.md (271 more words)Show less
Size-sensitive coding notes (learned the hard way)
- esbuild inlines
const-bound numeric literals into every use site but preservesletbindings. That makes a sharedletbound tempting for size, but it compiles to a mutable context slot the engine cannot fold into the comparison: hoisting the hottest bound insrc/grapheme.jsinto aletmeasured up to 25% slower on the count loop, and removing the three shared bounds was also smaller after gzip. Spell hot-path bounds out as literals. - Hot-loop helpers live near engine inlining cliffs — check them with
--trace-turbo-inlining(printsbytecode size: N) after any edit tocat()ornextState(). TurboFan refuses callees at 460 bytecodes (catsat at 453 before it was split intocat/catRare; falling off cost 15-45%), Maglev refuses at 100 (nextStatemust stay ≤ 99, worth 8-26% there). Measure the mid/low tiers withnode --no-turbofanandnode --jitless; a change can win big on TurboFan and still lose on Maglev. - Generator syntax is the deliberate choice over a class-based iterator: a class was 13–34% faster on V8 but cost ~+1.6 KiB Hermes bytecode after Babel class lowering (Hermes can't parse class syntax natively). Don't convert to a class without re-measuring V8, JSC, Hermes, and all three size metrics.
codePointAt()beat manualcharCodeAtsurrogate pairing on every measured engine (17–20% faster on Hermes) and is smaller. Don't "optimize" it back.
Metro warning (Hermes scripts)
Metro spawns a worker farm per invocation. When calling Metro programmatically (custom measurement pipelines, loops), always pass maxWorkers: 1 in the Metro.loadConfig overrides — a runaway farm once spawned enough node processes to freeze the machine. One-off runs of the repo's own scripts are fine.
© cometkim, 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 .claude/skills/benchmark of cometkim/unicode-segmenter.
Open the folder on GitHubat commit 5d3c738
Compare with similar skills
Benchmark 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 |
|---|---|---|---|---|---|---|
| Benchmark this skillcometkim/unicode-segmenter | 112 | — | ~1.3k | Automated safety check: Pass | MIT | |
| React Doctormakeplane/plane | 61k | 12 repos | ~657 | Automated safety check: Pass | AGPL-3.0 | |
| Fixing Motion Performanceibelick/ui-skills | 9.5k | 5 repos | ~1.4k | Automated safety check: Pass | MIT | |
| GSAP Performance Tuninggreensock/gsap-skills | 16k | 4 repos | ~1k | Automated safety check: Pass | MIT | |
| React Frontend Development Guidelinesdiet103/claude-code-infrastructure-showcase | 10k | 2 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Web Quality Auditaddyosmani/web-quality-skills | 2.9k | — | ~2.6k | Automated safety check: Pass | MIT |
Similar skills
Fixing Motion Performance
ibelick/ui-skills
Audits and fixes web animation performance: layout thrashing, work that belongs on the compositor, scroll-linked motion and costly blur effects.
GSAP Performance Tuning
greensock/gsap-skills
Guides the agent to keep GSAP animations smooth by animating transforms and opacity, batching DOM reads and writes, and avoiding layout-heavy properties.
React Frontend Development Guidelines
diet103/claude-code-infrastructure-showcase
Guidelines for React 18 and TypeScript apps covering Suspense data fetching, lazy loading, feature folders, MUI v7 styling, TanStack Router and performance.
Web Quality Audit
addyosmani/web-quality-skills
Run an evidence-led web quality audit covering performance, accessibility, SEO, best practices, and agentic browsing.
SEO Google
AgriciDaniel/codex-seo
Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic.
More from cometkim/unicode-segmenter
Codspeed Debug
cometkim/unicode-segmenter
Investigate CodSpeed CI performance reports on PRs — regressions or improbable improvements that don't reproduce locally.
Bench Records
cometkim/unicode-segmenter
Archive benchmark runs into benchmark/grapheme/records and regenerate the HTML report.
Unicode Data
cometkim/unicode-segmenter
Regenerate the generated Unicode data modules (src/graphemedata.js, generaldata.js, emojidata.js, test/unicodetestdata.js) and verify correctness.
Related
Categories
Questions about Benchmark
What does Benchmark do?
Measure runtime perf, bundle size, and memory impact of unicode-segmenter changes. Benchmark is an agent skill from cometkim/unicode-segmenter. Measure runtime perf, bundle size, and memory impact of unicode-segmenter changes.
When should I use Benchmark?
Benchmark fits situations like: evaluating any src/ change; running yarn perf / bundle-stats / memory-stats; comparing against a baseline revision; benchmark numbers look noisy.
How do I install Benchmark in Claude Code?
Run `npx skills add cometkim/unicode-segmenter --skill benchmark -a claude-code`. Or copy the skill folder (.claude/skills/benchmark in cometkim/unicode-segmenter) into .claude/skills/benchmark in your project. Claude Code loads it when a task matches its description.
How do I install Benchmark in Codex?
Run `npx skills add cometkim/unicode-segmenter --skill benchmark -a codex`. Or copy the skill folder (.claude/skills/benchmark in cometkim/unicode-segmenter) into .agents/skills/benchmark in your project. Codex loads it when a task matches its description.
Can I use Benchmark 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 cometkim/unicode-segmenter --skill benchmark -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/benchmark, .gemini/skills/benchmark, .github/skills/benchmark and .opencode/skills/benchmark in your project.
What does Benchmark need to run?
Going by SKILL.md and its folder, Benchmark needs the command-line tools its instructions call (yarn, node, bun and git).
Does Benchmark access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Benchmark 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 Benchmark use?
Benchmark 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 Benchmark use?
About 1.3k tokens (SKILL.md is roughly 5.2k 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 Benchmark?
Skills that share tags, products or a category with Benchmark: React Doctor (makeplane/plane, 61k stars), Fixing Motion Performance (ibelick/ui-skills, 9.5k stars), GSAP Performance Tuning (greensock/gsap-skills, 16k stars) and React Frontend Development Guidelines (diet103/claude-code-infrastructure-showcase, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Benchmark?
cometkim (a GitHub user) maintains it in cometkim/unicode-segmenter, which has 112 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on July 29, 2026.
Source: cometkim/unicode-segmenter on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.