Optimize For GPU
K-Dense-AI/scientific-agent-skills
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster.
Review a CV-CUDA operator's BENCHMARK coverage — drivers, layout axis, baselines, the basic-tier floor, row counts, and coverage statistics.
Install Review Op Bench Coverage
$ npx skills add CVCUDA/CV-CUDA --skill review-op-bench-coverage -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CVCUDA/CV-CUDA review-op-bench-coverage --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/CVCUDA/CV-CUDA.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/review-op-bench-coverage .claude/skills/review-op-bench-coverage && 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 "review-op-bench-coverage" agent skill from https://github.com/CVCUDA/CV-CUDA/tree/main/.agents/skills/review-op-bench-coverage into .claude/skills/review-op-bench-coverage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-op-bench-coverage", 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/CVCUDA/CV-CUDA/tree/main/.agents/skills/review-op-bench-coverageType 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 CVCUDA/CV-CUDA --skill review-op-bench-coverage -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CVCUDA/CV-CUDA review-op-bench-coverage --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CVCUDA/CV-CUDA.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/review-op-bench-coverage .agents/skills/review-op-bench-coverage && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "review-op-bench-coverage" agent skill from https://github.com/CVCUDA/CV-CUDA/tree/main/.agents/skills/review-op-bench-coverage into .agents/skills/review-op-bench-coverage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-op-bench-coverage", 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 CVCUDA/CV-CUDA --skill review-op-bench-coverage -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CVCUDA/CV-CUDA review-op-bench-coverage --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CVCUDA/CV-CUDA.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/review-op-bench-coverage .cursor/skills/review-op-bench-coverage && 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 "review-op-bench-coverage" agent skill from https://github.com/CVCUDA/CV-CUDA/tree/main/.agents/skills/review-op-bench-coverage into .cursor/skills/review-op-bench-coverage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-op-bench-coverage", 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/CVCUDA/CV-CUDA.git --path .agents/skills/review-op-bench-coverage--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 CVCUDA/CV-CUDA --skill review-op-bench-coverage -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CVCUDA/CV-CUDA review-op-bench-coverage --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CVCUDA/CV-CUDA.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/review-op-bench-coverage .gemini/skills/review-op-bench-coverage && 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 "review-op-bench-coverage" agent skill from https://github.com/CVCUDA/CV-CUDA/tree/main/.agents/skills/review-op-bench-coverage into .gemini/skills/review-op-bench-coverage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-op-bench-coverage", 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 CVCUDA/CV-CUDA review-op-bench-coverageInstalls 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 CVCUDA/CV-CUDA --skill review-op-bench-coverage -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/CVCUDA/CV-CUDA.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/review-op-bench-coverage .github/skills/review-op-bench-coverage && 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 "review-op-bench-coverage" agent skill from https://github.com/CVCUDA/CV-CUDA/tree/main/.agents/skills/review-op-bench-coverage into .github/skills/review-op-bench-coverage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-op-bench-coverage", 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 CVCUDA/CV-CUDA --skill review-op-bench-coverage -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install CVCUDA/CV-CUDA review-op-bench-coverage --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CVCUDA/CV-CUDA.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/review-op-bench-coverage .opencode/skills/review-op-bench-coverage && 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 "review-op-bench-coverage" agent skill from https://github.com/CVCUDA/CV-CUDA/tree/main/.agents/skills/review-op-bench-coverage into .opencode/skills/review-op-bench-coverage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-op-bench-coverage", 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
review-op-bench-coverage- GitHub stars
- 2.7k
- Token cost
- ~274 tokens
- SKILL.md length
- 92 words
- Files
- 1
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Review a CV-CUDA operator's BENCHMARK coverage — drivers, layout axis, baselines, the basic-tier floor, row counts, and coverage statistics.
- Asked whether an operators benchmarks/baselines are complete
- Calls python3
- Find/fill bench gaps
What it does
Review Op Bench Coverage is an agent skill from CVCUDA/CV-CUDA. Review a CV-CUDA operator's BENCHMARK coverage — drivers, layout axis, baselines, the basic-tier floor, row counts, and coverage statistics. Use when asked whether an operator's benchmarks/baselines are complete or to find/fill bench gaps.
Its SKILL.md is about 270 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 Data & Analytics, covering Statistics. It works with CUDA, NVIDIA AI Platform and Python. The repository describes itself as: CV-CUDA™ is an open-source, GPU accelerated library for cloud-scale image processing and computer vision.
When your agent uses it
- Asked whether an operators benchmarks/baselines are complete
- Find/fill bench gaps
Example prompts
- “/review-op-bench-coverage”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit b051f32. 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:
python3
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
Review Op Bench Coverage loads about 274 tokens when it runs. Until then it costs about 66 tokens; SKILL.md has 92 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
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 92 words (~274 tokens).
“[//]: # "SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved." [//]: # "SPDX-License-Identifier: Apache-2.0"”
- name
- review-op-bench-coverage
Files
Just SKILL.md in .agents/skills/review-op-bench-coverage of CVCUDA/CV-CUDA.
Open the folder on GitHubat commit b051f32
Compare with similar skills
Review Op Bench Coverage 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 |
|---|---|---|---|---|---|---|
| Review Op Bench Coverage this skillCVCUDA/CV-CUDA | 2.7k | — | ~274 | Automated safety check: Pass | Custom licence | |
| Optimize For GPUK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Megatron-LM on SLURMNVIDIA/Megatron-LM | 18k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Cutlass SkillslowlyC/agent-gpu-skills | 169 | — | ~1.3k | Automated safety check: Pass | MIT | |
| Triton SkillslowlyC/agent-gpu-skills | 169 | — | ~1.3k | Automated safety check: Pass | MIT | |
| Vllm Deploy Simplevllm-project/vllm-skills | 103 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 |
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More from CVCUDA/CV-CUDA
All 12 skills in this repoRefactor Op
CVCUDA/CV-CUDA
Find and safely apply per-operator refactoring / redundancy-reduction opportunities in a CV-CUDA operator (near-duplicate Tensor/VarShape kernels, reinvented shared utilities, dead code).
Optimize Op
CVCUDA/CV-CUDA
Drive a single-operator optimization campaign per .agents/guidance/OPTIMIZATIONGUIDELINES.md, with a deterministically enforced definition-of-done and versioned MR summary.
Review Op
CVCUDA/CV-CUDA
Review a CV-CUDA operator end-to-end (support / test / bench / docs coverage).
Optimize Op Verify
CVCUDA/CV-CUDA
Verify a CV-CUDA optimization campaign's deterministic definition-of-done and concise versioned MR summary per .agents/guidance/OPTIMIZATIONGUIDELINES.md.
Make Op
CVCUDA/CV-CUDA
Add a new CV-CUDA operator end-to-end per .agents/guidance/MAKEOPGUIDELINES.md, with a deterministically-enforced definition-of-done.
Make Op Scaffold
CVCUDA/CV-CUDA
Scaffold a new CV-CUDA operator — a complete, wired, building skeleton — and delegate the implementation to a human or another AI.
Related
Works with
Categories
Questions about Review Op Bench Coverage
What does Review Op Bench Coverage do?
Review a CV-CUDA operator's BENCHMARK coverage — drivers, layout axis, baselines, the basic-tier floor, row counts, and coverage statistics. Review Op Bench Coverage is an agent skill from CVCUDA/CV-CUDA. Review a CV-CUDA operator's BENCHMARK coverage — drivers, layout axis, baselines, the basic-tier floor, row counts, and coverage statistics.
When should I use Review Op Bench Coverage?
Review Op Bench Coverage fits situations like: asked whether an operators benchmarks/baselines are complete; find/fill bench gaps.
How do I install Review Op Bench Coverage in Claude Code?
Run `npx skills add CVCUDA/CV-CUDA --skill review-op-bench-coverage -a claude-code`. Or copy the skill folder (.agents/skills/review-op-bench-coverage in CVCUDA/CV-CUDA) into .claude/skills/review-op-bench-coverage in your project. Claude Code loads it when a task matches its description.
How do I install Review Op Bench Coverage in Codex?
Run `npx skills add CVCUDA/CV-CUDA --skill review-op-bench-coverage -a codex`. Or copy the skill folder (.agents/skills/review-op-bench-coverage in CVCUDA/CV-CUDA) into .agents/skills/review-op-bench-coverage in your project. Codex loads it when a task matches its description.
Can I use Review Op Bench Coverage 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 CVCUDA/CV-CUDA --skill review-op-bench-coverage -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-op-bench-coverage, .gemini/skills/review-op-bench-coverage, .github/skills/review-op-bench-coverage and .opencode/skills/review-op-bench-coverage in your project.
What does Review Op Bench Coverage need to run?
Going by SKILL.md and its folder, Review Op Bench Coverage needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Review Op Bench Coverage 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 Review Op Bench Coverage 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 Review Op Bench Coverage use?
Review Op Bench Coverage has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Review Op Bench Coverage use?
About 274 tokens (SKILL.md is roughly 1.1k 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 Review Op Bench Coverage?
Skills that share tags, products or a category with Review Op Bench Coverage: Optimize For GPU (K-Dense-AI/scientific-agent-skills, 48k stars), Megatron-LM on SLURM (NVIDIA/Megatron-LM, 18k stars), Cutlass Skill (slowlyC/agent-gpu-skills, 169 stars) and Triton Skill (slowlyC/agent-gpu-skills, 169 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Review Op Bench Coverage?
CVCUDA (a GitHub organization) maintains it in CVCUDA/CV-CUDA, which has 2,729 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on September 16, 2026.
Source: CVCUDA/CV-CUDA on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.