Add Uint Support
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
Unified kernel benchmarking protocol producing JSON results with latency, TFLOPS, GBps, and comparison against PyTorch baselines.
Install Kernel Benchmark
$ npx skills add ZJLi2013/awesome-kernel-skills --skill kernel-benchmark -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills kernel-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/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/system/benchmark .claude/skills/kernel-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 "kernel-benchmark" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/benchmark into .claude/skills/kernel-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kernel-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/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/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 ZJLi2013/awesome-kernel-skills --skill kernel-benchmark -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills kernel-benchmark --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/system/benchmark .agents/skills/kernel-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 "kernel-benchmark" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/benchmark into .agents/skills/kernel-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kernel-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 ZJLi2013/awesome-kernel-skills --skill kernel-benchmark -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills kernel-benchmark --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/system/benchmark .cursor/skills/kernel-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 "kernel-benchmark" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/benchmark into .cursor/skills/kernel-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kernel-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/ZJLi2013/awesome-kernel-skills.git --path skills/system/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 ZJLi2013/awesome-kernel-skills --skill kernel-benchmark -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills kernel-benchmark --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/system/benchmark .gemini/skills/kernel-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 "kernel-benchmark" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/benchmark into .gemini/skills/kernel-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kernel-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 ZJLi2013/awesome-kernel-skills kernel-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 ZJLi2013/awesome-kernel-skills --skill kernel-benchmark -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/system/benchmark .github/skills/kernel-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 "kernel-benchmark" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/benchmark into .github/skills/kernel-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kernel-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 ZJLi2013/awesome-kernel-skills --skill kernel-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 ZJLi2013/awesome-kernel-skills kernel-benchmark --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZJLi2013/awesome-kernel-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/system/benchmark .opencode/skills/kernel-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 "kernel-benchmark" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/system/benchmark into .opencode/skills/kernel-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kernel-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
kernel-benchmark- GitHub stars
- 102
- Token cost
- ~567 tokens
- SKILL.md length
- 181 words
- Files
- 1
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- None found
At a glance
Unified kernel benchmarking protocol producing JSON results with latency, TFLOPS, GBps, and comparison against PyTorch baselines.
- Works in 5 steps: Warmup → Timed Runs → Metrics Computation → …
- Benchmarking kernels
- SKILL.md covers Overview, Protocol and Agent Instructions
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Kernel Benchmark is an agent skill from ZJLi2013/awesome-kernel-skills. Unified kernel benchmarking protocol producing JSON results with latency, TFLOPS, GBps, and comparison against PyTorch baselines. Covers warmup, timing, and cross-platform reporting. Use when benchmarking kernels, measuring performance, or comparing implementations.
Its SKILL.md is about 570 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 AI & LLM Engineering, covering Deep learning. It works with PyTorch.
When your agent uses it
- Benchmarking kernels
- Measuring performance
- Comparing implementations
Example prompts
- “/kernel-benchmark”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
- Warmup
- Timed Runs
- Metrics Computation
- Baseline Comparison
- Output Format
What it can do on your machine
Read from SKILL.md and the folder at commit aba7662. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are python and json).
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
Kernel Benchmark loads about 567 tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 181 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
Without a licence we can't republish the file, so here is its outline and opening line. It has 181 words (~567 tokens).
“Consistent benchmarking methodology across all kernels and platforms. Every benchmark produces a JSON result for automated tracking.”
- name
- kernel-benchmark
Files
Just SKILL.md in skills/system/benchmark of ZJLi2013/awesome-kernel-skills.
Open the folder on GitHubat commit aba7662
Compare with similar skills
Kernel 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 |
|---|---|---|---|---|---|---|
| Kernel Benchmark this skillZJLi2013/awesome-kernel-skills | 102 | — | ~567 | Automated safety check: Pass | None | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Add Torch Shapes Examplefacebook/pyrefly | 7.1k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Interview Cheatsheetwanshuiyin/ARIS-in-AI-Offer | 574 | 1 repos | ~3.4k | Automated safety check: Notes | MIT | |
| Ghstack CIpytorch/pytorch | 104k | — | ~1.4k | Automated safety check: Pass | Custom licence |
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Add Torch Shapes Example
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Interview Cheatsheet
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More from ZJLi2013/awesome-kernel-skills
All 12 skills in this repoCross Entropy Kernel
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Optimize fused cross-entropy loss kernels in Triton for NVIDIA and AMD GPUs.
Flash Attention Kernel
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Optimize FlashAttention-style fused attention kernels in Triton for NVIDIA and AMD GPUs.
Fused Moe Kernel
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Optimize Fused Mixture-of-Experts (MoE) kernels in Triton for NVIDIA and AMD GPUs.
Gemm Kernel Optimization
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Optimize dense matrix multiplication (GEMM) kernels in Triton for NVIDIA and AMD GPUs.
Iterative Kernel Optimization Loop
ZJLi2013/awesome-kernel-skills
Orchestrates continuous kernel optimization by chaining profiling, bottleneck diagnosis, tier-based optimization, verification, and benchmarking into an iterative loop.
Kernel Profiling
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Profile GPU kernels using NCU (NVIDIA) or rocprof (AMD) to collect performance metrics.
Related
Works with
Categories
Questions about Kernel Benchmark
What does Kernel Benchmark do?
Unified kernel benchmarking protocol producing JSON results with latency, TFLOPS, GBps, and comparison against PyTorch baselines. Kernel Benchmark is an agent skill from ZJLi2013/awesome-kernel-skills. Unified kernel benchmarking protocol producing JSON results with latency, TFLOPS, GBps, and comparison against PyTorch baselines.
When should I use Kernel Benchmark?
Kernel Benchmark fits situations like: benchmarking kernels; measuring performance; comparing implementations.
How do I install Kernel Benchmark in Claude Code?
Run `npx skills add ZJLi2013/awesome-kernel-skills --skill kernel-benchmark -a claude-code`. Or copy the skill folder (skills/system/benchmark in ZJLi2013/awesome-kernel-skills) into .claude/skills/kernel-benchmark in your project. Claude Code loads it when a task matches its description.
How do I install Kernel Benchmark in Codex?
Run `npx skills add ZJLi2013/awesome-kernel-skills --skill kernel-benchmark -a codex`. Or copy the skill folder (skills/system/benchmark in ZJLi2013/awesome-kernel-skills) into .agents/skills/kernel-benchmark in your project. Codex loads it when a task matches its description.
Can I use Kernel 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 ZJLi2013/awesome-kernel-skills --skill kernel-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/kernel-benchmark, .gemini/skills/kernel-benchmark, .github/skills/kernel-benchmark and .opencode/skills/kernel-benchmark in your project.
What does Kernel Benchmark need to run?
SKILL.md names no scripts, command-line tools or credentials: Kernel Benchmark is instructions for the agent only. Our summary lists: Python 3.
Does Kernel Benchmark 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 Kernel 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 Kernel Benchmark use?
No licence was found for Kernel Benchmark or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Kernel Benchmark use?
About 567 tokens (SKILL.md is roughly 2.3k 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 Kernel Benchmark?
Skills that share tags, products or a category with Kernel Benchmark: Add Uint Support (pytorch/pytorch, 104k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Add Torch Shapes Example (facebook/pyrefly, 7.1k stars) and Interview Cheatsheet (wanshuiyin/ARIS-in-AI-Offer, 574 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Kernel Benchmark?
ZJLi2013 (a GitHub user) maintains it in ZJLi2013/awesome-kernel-skills, which has 102 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on March 31, 2026.
Source: ZJLi2013/awesome-kernel-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.