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Agent skill

Review Op

by CVCUDA in CVCUDA/CV-CUDA

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

Custom licenceAuto-check passedTesting & QA

Install Review Op

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

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

GitHub CLI
$ gh skill install CVCUDA/CV-CUDA review-op --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 .claude/skills/review-op && 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
GitHub stars
2.7k
Token cost
~481 tokens
SKILL.md length
190 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
Custom licence

At a glance

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

  • Works in 4 steps: Run python3 tools/review_op.py (scope with → Interpret each finding by its cited item… → If the user asked to fix (--fix), apply… → …
  • The user asks to review an operator
  • SKILL.md covers Workflow and Prompt Handling
  • Calls python3

What it does

Review Op is an agent skill from CVCUDA/CV-CUDA. Review a CV-CUDA operator end-to-end (support / test / bench / docs coverage). Use when the user asks to review an operator, audit its input-type/layout/dtype support, test coverage, benchmark coverage, or docs/API, or to find & fix per-operator coverage gaps. Produces a deterministic findings-first report.

Its SKILL.md is about 480 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 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

  • The user asks to review an operator
  • Audit its input-type/layout/dtype support
  • Benchmark coverage
  • Find & fix per-operator coverage gaps

Example prompts

  • “/review-op”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Run python3 tools/review_op.py (scope with
  2. Interpret each finding by its cited item id in .agents/guidance/REVIEW_OP_GUIDELINES.md. Resolve every
  3. If the user asked to fix (--fix), apply the corrective action named per GAP in
  4. Return findings-first: GAPs, then unresolved MANUALs, then RECOMMENDATIONs, then the

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 loads about 481 tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 190 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~481

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 190 words (~481 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

Read the full SKILL.md on GitHub

Files

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

Open the folder on GitHubat commit b051f32

Compare with similar skills

Review Op 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 compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Op this skillCVCUDA/CV-CUDA2.7k—~481Automated safety check: PassCustom licence
Jetson Video SetupNVIDIA/skills3.5k1 repos~2.4kAutomated safety check: NotesApache-2.0
Cutlass SkillslowlyC/agent-gpu-skills169—~1.3kAutomated safety check: PassMIT
Triton SkillslowlyC/agent-gpu-skills169—~1.3kAutomated safety check: PassMIT
Vllm Deploy Simplevllm-project/vllm-skills103—~1.6kAutomated safety check: PassApache-2.0
Quark Env Preflightamd/Quark181—~1.4kAutomated safety check: PassMIT

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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).

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  • Optimize Op

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  • Optimize Op Verify

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    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
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  • 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.

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  • Make Op Scaffold

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  • Make Op Verify

    CVCUDA/CV-CUDA

    Verify a new CV-CUDA operator against the deterministic final regression checklist (the /make-op done-gate).

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Works with

Categories

Questions about Review Op

What does Review Op do?

Review a CV-CUDA operator end-to-end (support / test / bench / docs coverage). Review Op is an agent skill from CVCUDA/CV-CUDA. Review a CV-CUDA operator end-to-end (support / test / bench / docs coverage).

When should I use Review Op?

Review Op fits situations like: the user asks to review an operator; audit its input-type/layout/dtype support; benchmark coverage; find & fix per-operator coverage gaps.

How do I install Review Op in Claude Code?

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

How do I install Review Op in Codex?

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

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

What does Review Op need to run?

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

Does Review Op 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 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 use?

Review Op 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 use?

About 481 tokens (SKILL.md is roughly 1.9k 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?

Skills that share tags, products or a category with Review Op: Jetson Video Setup (NVIDIA/skills, 3.5k stars), Cutlass Skill (slowlyC/agent-gpu-skills, 169 stars), Triton Skill (slowlyC/agent-gpu-skills, 169 stars) and Vllm Deploy Simple (vllm-project/vllm-skills, 103 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Op?

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.