Optimize dense matrix multiplication (GEMM) kernels in Triton for NVIDIA and AMD GPUs.
$ npx skills add ZJLi2013/awesome-kernel-skills --skill gemm-kernel-optimization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills gemm-kernel-optimization --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/kernels/gemm .claude/skills/gemm-kernel-optimization && 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 "gemm-kernel-optimization" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/gemm into .claude/skills/gemm-kernel-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemm-kernel-optimization", 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/kernels/gemmType 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 gemm-kernel-optimization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills gemm-kernel-optimization --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/kernels/gemm .agents/skills/gemm-kernel-optimization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gemm-kernel-optimization" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/gemm into .agents/skills/gemm-kernel-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemm-kernel-optimization", 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 gemm-kernel-optimization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills gemm-kernel-optimization --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/kernels/gemm .cursor/skills/gemm-kernel-optimization && 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 "gemm-kernel-optimization" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/gemm into .cursor/skills/gemm-kernel-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemm-kernel-optimization", 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/kernels/gemm--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 gemm-kernel-optimization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills gemm-kernel-optimization --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/kernels/gemm .gemini/skills/gemm-kernel-optimization && 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 "gemm-kernel-optimization" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/gemm into .gemini/skills/gemm-kernel-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemm-kernel-optimization", 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 gemm-kernel-optimizationInstalls 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 gemm-kernel-optimization -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/kernels/gemm .github/skills/gemm-kernel-optimization && 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 "gemm-kernel-optimization" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/gemm into .github/skills/gemm-kernel-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemm-kernel-optimization", 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 gemm-kernel-optimization -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 gemm-kernel-optimization --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/kernels/gemm .opencode/skills/gemm-kernel-optimization && 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 "gemm-kernel-optimization" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/gemm into .opencode/skills/gemm-kernel-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gemm-kernel-optimization", 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.
gemm-kernel-optimizationOptimize dense matrix multiplication (GEMM) kernels in Triton for NVIDIA and AMD GPUs.
Gemm Kernel Optimization is an agent skill from ZJLi2013/awesome-kernel-skills. Optimize dense matrix multiplication (GEMM) kernels in Triton for NVIDIA and AMD GPUs. Covers tiled blocking, L2 cache grouping, tensor core utilization, and dual-platform autotune. Use when writing or optimizing matmul, linear layers, or any GEMM-based kernel.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `test_gemm.py` and `triton_template.py`).
It sits in AI & LLM Engineering, covering GPU and accelerator computing. It works with NVIDIA AI Platform.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit aba7662. 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.
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
triton-lang.orggithub.comresearch.colfax-intl.comFrom 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.
Gemm Kernel Optimization loads about 1.1k tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 357 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.
Without a licence we can't republish the file, so here is its outline and opening line. It has 357 words (~1,112 tokens).
“General Matrix Multiply (C = A @ B) is the most compute-intensive operation in deep learning. GEMM is compute-bound: the key metric is TFLOPS (target: >60% of hardware peak).”
SKILL.md and 2 other files in skills/kernels/gemm of ZJLi2013/awesome-kernel-skills.
Open the folder on GitHubat commit aba7662
Gemm Kernel Optimization 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 |
|---|---|---|---|---|---|---|
| Gemm Kernel Optimization this skillZJLi2013/awesome-kernel-skills | 102 | — | ~1.1k | Automated safety check: Pass | None | |
| Fla Triton To Gluonfla-org/flash-linear-attention | 5.8k | — | ~4.2k | Automated safety check: Pass | MIT | |
| DGX Spark Memory and Thermal Opswshobson/agents | 40k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| DGX Spark Training Gotchaswshobson/agents | 40k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| Megatron-LM on SLURMNVIDIA/Megatron-LM | 18k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Physical AI Video Augmentation on OSMONVIDIA/skills | 3.5k | — | ~4.7k | Automated safety check: Notes | Apache-2.0 |
fla-org/flash-linear-attention
Workflow for porting an existing Triton kernel in fla/ops/ to Gluon (triton.experimental.gluon) to gain explicit control over tensor layouts, shared memory, async data movement (cp.async / TMA), MMA…
wshobson/agents
Plans memory headroom, works through out-of-memory failures and watches temperature and power during long ML training jobs on NVIDIA DGX Spark.
wshobson/agents
Preflight checks and diagnosis for ten known failure modes of ML training on NVIDIA DGX Spark's GB10, spanning launch errors, memory, thermals, bandwidth and precision.
NVIDIA/Megatron-LM
Shows how to launch distributed Megatron-LM training on a SLURM cluster: sbatch skeleton, torch.distributed.run setup, CUDA_DEVICE_MAX_CONNECTIONS rules and failure diagnosis.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
Orchestra-Research/AI-Research-SKILLs
Fine-tunes and evaluates OpenVLA-OFT and OFT+ robot policies with LoRA and continuous action heads on LIBERO simulation and ALOHA real-robot setups.
ZJLi2013/awesome-kernel-skills
Optimize fused cross-entropy loss kernels in Triton for NVIDIA and AMD GPUs.
ZJLi2013/awesome-kernel-skills
Optimize FlashAttention-style fused attention kernels in Triton for NVIDIA and AMD GPUs.
ZJLi2013/awesome-kernel-skills
Optimize Fused Mixture-of-Experts (MoE) kernels in Triton for NVIDIA and AMD GPUs.
ZJLi2013/awesome-kernel-skills
Orchestrates continuous kernel optimization by chaining profiling, bottleneck diagnosis, tier-based optimization, verification, and benchmarking into an iterative loop.
ZJLi2013/awesome-kernel-skills
Unified kernel benchmarking protocol producing JSON results with latency, TFLOPS, GBps, and comparison against PyTorch baselines.
ZJLi2013/awesome-kernel-skills
Profile GPU kernels using NCU (NVIDIA) or rocprof (AMD) to collect performance metrics.
Works with
Categories
Optimize dense matrix multiplication (GEMM) kernels in Triton for NVIDIA and AMD GPUs. Gemm Kernel Optimization is an agent skill from ZJLi2013/awesome-kernel-skills. Optimize dense matrix multiplication (GEMM) kernels in Triton for NVIDIA and AMD GPUs.
Gemm Kernel Optimization fits situations like: optimizing matmul; any GEMM-based kernel.
Run `npx skills add ZJLi2013/awesome-kernel-skills --skill gemm-kernel-optimization -a claude-code`. Or copy the skill folder (skills/kernels/gemm in ZJLi2013/awesome-kernel-skills) into .claude/skills/gemm-kernel-optimization in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ZJLi2013/awesome-kernel-skills --skill gemm-kernel-optimization -a codex`. Or copy the skill folder (skills/kernels/gemm in ZJLi2013/awesome-kernel-skills) into .agents/skills/gemm-kernel-optimization 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 ZJLi2013/awesome-kernel-skills --skill gemm-kernel-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gemm-kernel-optimization, .gemini/skills/gemm-kernel-optimization, .github/skills/gemm-kernel-optimization and .opencode/skills/gemm-kernel-optimization in your project.
Going by SKILL.md and its folder, Gemm Kernel Optimization needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md names 3 domains. As links in the text: triton-lang.org, github.com and research.colfax-intl.com. 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.
No licence was found for Gemm Kernel Optimization or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
About 1.1k tokens (SKILL.md is roughly 4.4k 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 Gemm Kernel Optimization: Fla Triton To Gluon (fla-org/flash-linear-attention, 5.8k stars), DGX Spark Memory and Thermal Ops (wshobson/agents, 40k stars), DGX Spark Training Gotchas (wshobson/agents, 40k stars) and Megatron-LM on SLURM (NVIDIA/Megatron-LM, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.
