TAO Image Embeddings
NVIDIA/skills
Turns a parquet of image file paths into a parquet of embeddings with CLIP, SigLIP or a TAO checkpoint, using the TAO Data Services container, ahead of neighbor mining.
Optimize Rotary Position Embedding (RoPE) kernels in Triton for NVIDIA and AMD GPUs.
Install Rotary Embedding Kernel
$ npx skills add ZJLi2013/awesome-kernel-skills --skill rotary-embedding-kernel -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills rotary-embedding-kernel --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/rotary-embedding .claude/skills/rotary-embedding-kernel && 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 "rotary-embedding-kernel" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/rotary-embedding into .claude/skills/rotary-embedding-kernel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rotary-embedding-kernel", 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/rotary-embeddingType 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 rotary-embedding-kernel -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills rotary-embedding-kernel --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/rotary-embedding .agents/skills/rotary-embedding-kernel && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "rotary-embedding-kernel" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/rotary-embedding into .agents/skills/rotary-embedding-kernel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rotary-embedding-kernel", 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 rotary-embedding-kernel -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills rotary-embedding-kernel --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/rotary-embedding .cursor/skills/rotary-embedding-kernel && 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 "rotary-embedding-kernel" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/rotary-embedding into .cursor/skills/rotary-embedding-kernel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rotary-embedding-kernel", 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/rotary-embedding--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 rotary-embedding-kernel -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ZJLi2013/awesome-kernel-skills rotary-embedding-kernel --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/rotary-embedding .gemini/skills/rotary-embedding-kernel && 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 "rotary-embedding-kernel" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/rotary-embedding into .gemini/skills/rotary-embedding-kernel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rotary-embedding-kernel", 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 rotary-embedding-kernelInstalls 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 rotary-embedding-kernel -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/rotary-embedding .github/skills/rotary-embedding-kernel && 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 "rotary-embedding-kernel" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/rotary-embedding into .github/skills/rotary-embedding-kernel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rotary-embedding-kernel", 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 rotary-embedding-kernel -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 rotary-embedding-kernel --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/rotary-embedding .opencode/skills/rotary-embedding-kernel && 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 "rotary-embedding-kernel" agent skill from https://github.com/ZJLi2013/awesome-kernel-skills/tree/main/skills/kernels/rotary-embedding into .opencode/skills/rotary-embedding-kernel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rotary-embedding-kernel", 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
rotary-embedding-kernel- GitHub stars
- 102
- Token cost
- ~421 tokens
- SKILL.md length
- 154 words
- Files
- 3
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- None found
At a glance
Optimize Rotary Position Embedding (RoPE) kernels in Triton for NVIDIA and AMD GPUs.
- Works in 2 steps: Interleaved: [x0, x1, x2, x3, ...] where… → Split-half: [x_first_half |…
- Optimizing RoPE
- SKILL.md covers Overview, Core Technique, Verification and Common Pitfalls, plus 1 more section
- Runs Python scripts from its folder; calls python
What it does
Rotary Embedding Kernel is an agent skill from ZJLi2013/awesome-kernel-skills. Optimize Rotary Position Embedding (RoPE) kernels in Triton for NVIDIA and AMD GPUs. Covers interleaved/sequential layouts, sincos precomputation, and multi-row processing. Use when writing or optimizing RoPE, position embeddings, or rotary transformations.
Its SKILL.md is about 420 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `test_rotary.py` and `triton_template.py`).
It sits in AI & LLM Engineering, covering Embeddings. It works with NVIDIA AI Platform.
When your agent uses it
- Optimizing RoPE
- Position embeddings
- Rotary transformations
Example prompts
- “/rotary-embedding-kernel”
Requirements
- Python 3
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- Interleaved: [x0, x1, x2, x3, ...] where pairs are (x0,x1), (x2,x3), ...
- Split-half: [x_first_half | x_second_half] where pairs are (x[i], x[i+D/2])
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
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
arxiv.orggithub.com
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
Rotary Embedding Kernel loads about 421 tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 154 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 154 words (~421 tokens).
“RoPE applies a rotation to pairs of elements: (x1, x2) -> (x1*cos - x2*sin, x1*sin + x2*cos). Applied per-position in the sequence dimension.”
- name
- rotary-embedding-kernel
Files
SKILL.md and 2 other files in skills/kernels/rotary-embedding of ZJLi2013/awesome-kernel-skills.
- SKILL.md
- test_rotary.py
- triton_template.py
Open the folder on GitHubat commit aba7662
Compare with similar skills
Rotary Embedding Kernel 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 |
|---|---|---|---|---|---|---|
| Rotary Embedding Kernel this skillZJLi2013/awesome-kernel-skills | 102 | — | ~421 | Automated safety check: Pass | None | |
| TAO Image EmbeddingsNVIDIA/skills | 3.5k | — | ~2k | Automated safety check: Notes | Apache-2.0 | |
| Tao Train Metric Learning RecognitionNVIDIA/skills | 3.5k | — | ~2.9k | Automated safety check: Notes | Apache-2.0 | |
| Embeddings via 9Routerdecolua/9router | 30k | — | ~604 | Automated safety check: Pass | MIT | |
| Keiroutermydisha/keirouter | 147 | — | ~995 | Automated safety check: Pass | MIT | |
| Keirouter Embeddingsmydisha/keirouter | 147 | — | ~577 | Automated safety check: Pass | MIT |
Similar skills
- Official
- Official
Tao Train Metric Learning Recognition
NVIDIA/skills
Metric-learning recognition (ml-recog) for fine-grained visual recognition.
Embeddings via 9Router
decolua/9router
Generates vector embeddings through the 9Router /v1/embeddings endpoint, using models from providers such as OpenAI, Gemini, Mistral and Voyage for RAG and semantic search.
Keirouter
mydisha/keirouter
Entry point for KeiRouter — local/remote AI gateway with OpenAI-compatible REST for chat, image, TTS, embeddings, web search, web fetch.
Keirouter Embeddings
mydisha/keirouter
Generate vector embeddings via KeiRouter /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia embedding models for RAG, semantic search, similarity.
Testing Prompt Injection In RAG Pipelines
mukul975/Anthropic-Cybersecurity-Skills
Probes Retrieval-Augmented Generation pipelines for indirect prompt injection via poisoned retrieved documents and embedding-space manipulation, using NVIDIA garak, Promptfoo red-team plugins, and…
More from ZJLi2013/awesome-kernel-skills
All 12 skills in this repoCross Entropy Kernel
ZJLi2013/awesome-kernel-skills
Optimize fused cross-entropy loss kernels in Triton for NVIDIA and AMD GPUs.
Flash Attention Kernel
ZJLi2013/awesome-kernel-skills
Optimize FlashAttention-style fused attention kernels in Triton for NVIDIA and AMD GPUs.
Fused Moe Kernel
ZJLi2013/awesome-kernel-skills
Optimize Fused Mixture-of-Experts (MoE) kernels in Triton for NVIDIA and AMD GPUs.
Gemm Kernel Optimization
ZJLi2013/awesome-kernel-skills
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 Benchmark
ZJLi2013/awesome-kernel-skills
Unified kernel benchmarking protocol producing JSON results with latency, TFLOPS, GBps, and comparison against PyTorch baselines.
Related
Works with
Categories
Questions about Rotary Embedding Kernel
What does Rotary Embedding Kernel do?
Optimize Rotary Position Embedding (RoPE) kernels in Triton for NVIDIA and AMD GPUs. Rotary Embedding Kernel is an agent skill from ZJLi2013/awesome-kernel-skills. Optimize Rotary Position Embedding (RoPE) kernels in Triton for NVIDIA and AMD GPUs.
When should I use Rotary Embedding Kernel?
Rotary Embedding Kernel fits situations like: optimizing RoPE; position embeddings; rotary transformations.
How do I install Rotary Embedding Kernel in Claude Code?
Run `npx skills add ZJLi2013/awesome-kernel-skills --skill rotary-embedding-kernel -a claude-code`. Or copy the skill folder (skills/kernels/rotary-embedding in ZJLi2013/awesome-kernel-skills) into .claude/skills/rotary-embedding-kernel in your project. Claude Code loads it when a task matches its description.
How do I install Rotary Embedding Kernel in Codex?
Run `npx skills add ZJLi2013/awesome-kernel-skills --skill rotary-embedding-kernel -a codex`. Or copy the skill folder (skills/kernels/rotary-embedding in ZJLi2013/awesome-kernel-skills) into .agents/skills/rotary-embedding-kernel in your project. Codex loads it when a task matches its description.
Can I use Rotary Embedding Kernel 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 rotary-embedding-kernel -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rotary-embedding-kernel, .gemini/skills/rotary-embedding-kernel, .github/skills/rotary-embedding-kernel and .opencode/skills/rotary-embedding-kernel in your project.
What does Rotary Embedding Kernel need to run?
Going by SKILL.md and its folder, Rotary Embedding Kernel needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Rotary Embedding Kernel access the network?
SKILL.md names 2 domains. As links in the text: arxiv.org and github.com. This is read from the text; nothing was executed.
Is Rotary Embedding Kernel 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 Rotary Embedding Kernel use?
No licence was found for Rotary Embedding Kernel or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Rotary Embedding Kernel use?
About 421 tokens (SKILL.md is roughly 1.7k 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 Rotary Embedding Kernel?
Skills that share tags, products or a category with Rotary Embedding Kernel: TAO Image Embeddings (NVIDIA/skills, 3.5k stars), Tao Train Metric Learning Recognition (NVIDIA/skills, 3.5k stars), Embeddings via 9Router (decolua/9router, 30k stars) and Keirouter (mydisha/keirouter, 147 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Rotary Embedding Kernel?
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.