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Agent skill

Review Op Test Coverage

by CVCUDA in CVCUDA/CV-CUDA

Review a CV-CUDA operator's test coverage, including C++ correctness, required cross-layout parity, correctness rigor, and the Python API surface.

Custom licenceAuto-check passedTesting & QA

Install Review Op Test Coverage

skills CLI
$ npx skills add CVCUDA/CV-CUDA --skill review-op-test-coverage -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install CVCUDA/CV-CUDA review-op-test-coverage --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-test-coverage .claude/skills/review-op-test-coverage && rm -rf skills-src

Use ~/.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/

Facts

Skill name
review-op-test-coverage
GitHub stars
2.7k
Token cost
~264 tokens
SKILL.md length
86 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
Custom licence

At a glance

Review a CV-CUDA operator's test coverage, including C++ correctness, required cross-layout parity, correctness rigor, and the Python API surface.

  • Asked whether an operator is adequately tested
  • Calls python3
  • Find and fill test gaps

What it does

Review Op Test Coverage is an agent skill from CVCUDA/CV-CUDA. Review a CV-CUDA operator's test coverage, including C++ correctness, required cross-layout parity, correctness rigor, and the Python API surface. Use when asked whether an operator is adequately tested or to find and fill test gaps.

Its SKILL.md is about 260 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 Testing & QA, covering Test coverage. It works with Python, CUDA, C++ and NVIDIA AI Platform. 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 operator is adequately tested
  • Find and fill test gaps

Example prompts

  • “/review-op-test-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 Test Coverage loads about 264 tokens when it runs. Until then it costs about 64 tokens; SKILL.md has 86 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~264

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 86 words (~264 tokens).

“[//]: # "SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved." [//]: # "SPDX-License-Identifier: Apache-2.0"”

— opening of SKILL.md by CVCUDA, Custom licence
name
review-op-test-coverage

Read the full SKILL.md on GitHub

Files

Just SKILL.md in .agents/skills/review-op-test-coverage of CVCUDA/CV-CUDA.

Open the folder on GitHubat commit b051f32

Compare with similar skills

Review Op Test 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.

Review Op Test Coverage compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Op Test Coverage this skillCVCUDA/CV-CUDA2.7k—~264Automated safety check: PassCustom licence
Cutlass SkillslowlyC/agent-gpu-skills169—~1.3kAutomated safety check: PassMIT
Cudaq GuideNVIDIA/skills3.5k—~1.3kAutomated safety check: PassApache-2.0
Cuopt DeveloperNVIDIA/skills3.5k—~3.2kAutomated safety check: NotesApache-2.0
Holoscan Install CondaNVIDIA/skills3.5k—~2kAutomated safety check: PassApache-2.0
Holoscan Install DebianNVIDIA/skills3.5k—~1.6kAutomated safety check: NotesApache-2.0

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More from CVCUDA/CV-CUDA

All 12 skills in this repo

  • Refactor 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).

    2.7k GitHub stars~1.5k tokensUpdated 23 days ago
    Auto-check passed

  • 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.

    2.7k GitHub stars~834 tokensUpdated 23 days ago
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  • Review Op

    CVCUDA/CV-CUDA

    Review a CV-CUDA operator end-to-end (support / test / bench / docs coverage).

    2.7k GitHub stars~481 tokensUpdated 23 days ago
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  • 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.

    2.7k GitHub stars~424 tokensUpdated 23 days ago
    Auto-check passed

  • 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.

    2.7k GitHub stars~831 tokensUpdated 23 days ago
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  • 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.

    2.7k GitHub stars~306 tokensUpdated 23 days ago
    Auto-check passed

Works with

Categories

Questions about Review Op Test Coverage

What does Review Op Test Coverage do?

Review a CV-CUDA operator's test coverage, including C++ correctness, required cross-layout parity, correctness rigor, and the Python API surface. Review Op Test Coverage is an agent skill from CVCUDA/CV-CUDA. Review a CV-CUDA operator's test coverage, including C++ correctness, required cross-layout parity, correctness rigor, and the Python API surface.

When should I use Review Op Test Coverage?

Review Op Test Coverage fits situations like: asked whether an operator is adequately tested; find and fill test gaps.

How do I install Review Op Test Coverage in Claude Code?

Run `npx skills add CVCUDA/CV-CUDA --skill review-op-test-coverage -a claude-code`. Or copy the skill folder (.agents/skills/review-op-test-coverage in CVCUDA/CV-CUDA) into .claude/skills/review-op-test-coverage in your project. Claude Code loads it when a task matches its description.

How do I install Review Op Test Coverage in Codex?

Run `npx skills add CVCUDA/CV-CUDA --skill review-op-test-coverage -a codex`. Or copy the skill folder (.agents/skills/review-op-test-coverage in CVCUDA/CV-CUDA) into .agents/skills/review-op-test-coverage in your project. Codex loads it when a task matches its description.

Can I use Review Op Test 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-test-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-test-coverage, .gemini/skills/review-op-test-coverage, .github/skills/review-op-test-coverage and .opencode/skills/review-op-test-coverage in your project.

What does Review Op Test Coverage need to run?

Going by SKILL.md and its folder, Review Op Test Coverage needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Review Op Test 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 Test 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 Test Coverage use?

Review Op Test 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 Test Coverage use?

About 264 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 Test Coverage?

Skills that share tags, products or a category with Review Op Test Coverage: Cutlass Skill (slowlyC/agent-gpu-skills, 169 stars), Cudaq Guide (NVIDIA/skills, 3.5k stars), Cuopt Developer (NVIDIA/skills, 3.5k stars) and Holoscan Install Conda (NVIDIA/skills, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Op Test 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.