Agent skill

Vet

by atilladeniz in atilladeniz/Kubeli

Run vet immediately after ANY logical unit of code changes. An agent skill from atilladeniz/Kubeli.

MITAuto-check passedDevOps & Cloud

Install Vet

skills CLI
$ npx skills add atilladeniz/Kubeli --skill vet -a claude-code

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

GitHub CLI
$ gh skill install atilladeniz/Kubeli vet --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/atilladeniz/Kubeli.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/vet .claude/skills/vet && 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
vet
GitHub stars
387
Used in
2 other repos
Token cost
~1.6k tokens
SKILL.md length
817 words
Files
4 (incl. scripts)
Skills in repo
3
Repo updated
First seen
Licence
MIT

At a glance

Run vet immediately after ANY logical unit of code changes. An agent skill from atilladeniz/Kubeli.

  • Works in 3 steps: Run: opencode session list --format json… → Identify the current session from the… → Pass the session ID as --session-id.
  • Tasks that involve Container orchestration
  • SKILL.md covers Installation, Running Vet, Interpreting Results and Common Options, plus 2 more sections
  • Runs Python scripts from its folder; calls pip, pipx and uv

What it does

Vet is an agent skill from atilladeniz/Kubeli. Run vet immediately after ANY logical unit of code changes. Do not batch your changes, do not wait to be asked to run vet, make sure you are proactive.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/export_claude_code_session.py`, `scripts/export_codex_session.py` and `scripts/export_opencode_session.py`).

It sits in DevOps & Cloud, covering Container orchestration. It works with Kubernetes and Python. The repository describes itself as: A modern Kubernetes GUI management desktop app for macOS & Windows. Multi-cluster support, real-time monitoring, AI assistant, terminal access, and more. The licence is MIT.

When your agent uses it

  • Tasks that involve Container orchestration

Example prompts

  • “/vet”

Requirements

  • Python 3

Workflow steps

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

  1. Run: opencode session list --format json to list recent sessions with their IDs and titles.
  2. Identify the current session from the list by matching the title or timestamp.
  3. Pass the session ID as --session-id.

What it can do on your machine

Read from SKILL.md and the folder at commit 444659b. 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 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip
    • pipx
    • uv
    • opencode

    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):

    • github.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

Vet loads about 1.6k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 817 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from atilladeniz/Kubeli at commit 444659b, republished under its MIT licence (© atilladeniz). 817 words, ~1,603 tokens.

Download SKILL.mdSave it as .claude/skills/vet/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
vet
description
Run vet immediately after ANY logical unit of code changes. Do not batch your changes, do not wait to be asked to run vet, make sure you are proactive.

Vet

Run vet immediately after ANY logical unit of code changes. Do not batch changes, do not wait to be asked to run vet, make sure you are proactive.

Vet reviews git diffs and conversation history to find issues in code changes and conversation history. It is most effective when run frequently with conversation history, which helps it catch misunderstandings between what was requested and what was implemented. Despite this, vet is not a replacement for running tests.

Installation

Install vet using pip, pipx, or uv:

bash
# Using pip
pip install verify-everything

# Using pipx
pipx install verify-everything

# Using uv
uv tool install verify-everything

# Verify installation:
vet --help

Running Vet

Standard Usage

Before running vet, determine the correct Python binary:

bash
$(command -v python3 || command -v python)

Use whichever resolves (prefer python3). The examples below use python3, substitute python if that is what your system provides.

OpenCode:

bash
vet "goal" --history-loader "python3 ~/.agents/skills/vet/scripts/export_opencode_session.py --session-id <ses_ID>"

Codex:

bash
vet "goal" --history-loader "python3 ~/.codex/skills/vet/scripts/export_codex_session.py --session-file <path-to-session.jsonl>"

Claude Code:

bash
vet "goal" --history-loader "python3 ~/.claude/skills/vet/scripts/export_claude_code_session.py --session-file <path-to-session.jsonl>"

Without Conversation History

bash
vet "goal"
Finding Your Session

You should only search for sessions from your coding harness. If a user requests you use a different harness, they are likely referring to vet's agentic mode, not the session.

OpenCode: The --session-id argument requires a ses_... session ID. To find the current session ID:

  1. Run: opencode session list --format json to list recent sessions with their IDs and titles.
  2. Identify the current session from the list by matching the title or timestamp.
    • IMPORTANT: Verify the session you found matches the current conversation. If the title is ambiguous, compare timestamps or check multiple candidates.
  3. Pass the session ID as --session-id.

Codex: Session files are stored in ~/.codex/sessions/YYYY/MM/DD/. To find the correct session file:

  1. Find the most unique sentence / question / string in the current conversation.
  2. Run: grep -rl "UNIQUE_MESSAGE" ~/.codex/sessions/ to find the matching session file.
    • IMPORTANT: Verify the conversation you found matches the current conversation and that it is not another conversation with the same search string.
  3. Pass the matched file path as --session-file.

Claude Code: Session files are stored in ~/.claude/projects/<encoded-path>/. The encoded path replaces / with - (e.g. /home/user/myproject becomes -home-user-myproject). To find the correct session file:

  1. Find the most unique sentence / question / string in the current conversation.
  2. Run: grep -rl "UNIQUE_MESSAGE" ~/.claude/projects/ to find the matching session file.
    • IMPORTANT: Verify the conversation you found matches the current conversation and that it is not another conversation with the same search string.
  3. Pass the matched file path as --session-file.

NOTE: The examples in the standard usage section assume the user installed the vet skill at the user level, not the project level. Prior to trying to run vet, check if it was installed at the project level which should take precedence over the user level. If it is installed at the project level, ensure the history-loader option points to the correct location.

Interpreting Results

Vet analyzes the full git diff from the base commit. This may include changes from other agents or sessions working in the same repository. If vet reports issues that relate to changes you did not make in this session, disregard them, assuming they belong to another agent or the user.

Show full SKILL.md (328 more words)Show less

Common Options

  • --base-commit REF: Git ref for diff base (default: HEAD)
  • --model MODEL: LLM to use (default: claude-opus-4-6)
  • --list-models: list all models that are supported by vet
    • Run vet --help and look at the vet repo's readme for details about defining custom OpenAI-compatible models.
  • --update-models: fetch the latest community model definitions from the remote registry and cache them locally. See "Updating the Model Registry" below for when to run this.
  • --confidence-threshold N: Minimum confidence 0.0-1.0 (default: 0.8)
  • --output-format FORMAT: Output as text, json, or github
  • --quiet: Suppress status messages and 'No issues found.'
  • --agentic: Mode that routes analysis through the locally installed Claude Code or Codex CLI instead of calling the API directly. Try this if vet fails due to missing API keys. This is slower so it is not the default, but it often results in higher precision issue identification. --model is forwarded to the harness but not validated by vet, as vet doesn't know which models each harness supports.
  • --agent-harness: The two options for this are codex and claude. Claude Code is the default.
  • --help: Show comprehensive list of options

Updating

The vet CLI, skill files, and export scripts can become outdated as agent harnesses and LLM APIs change.

If this happens, try updating them. Run which vet to determine how vet was installed and update accordingly. For the skill files, check which skill directories exist on disk and update them with the latest versions from https://github.com/imbue-ai/vet/tree/main/skills/vet.

Updating the Model Registry

Run vet --update-models to fetch the latest community model definitions from the remote registry without upgrading vet itself. This caches model definitions locally so they appear in --list-models and can be used with --model.

You should run vet --update-models when:

  • Vet reports an unknown or unrecognized model error.
  • vet --list-models does not show a model you or the user expects to be available.
  • The user explicitly asks you to update the model registry.

Additional Information

Additional information can be found in the vet repo:

https://github.com/imbue-ai/vet

© atilladeniz, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts) in .claude/skills/vet of atilladeniz/Kubeli.

  • SKILL.md
  • scripts/export_claude_code_session.py
  • scripts/export_codex_session.py
  • scripts/export_opencode_session.py

Open the folder on GitHubat commit 444659b

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in atilladeniz/Kubeli, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Vet 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.

Vet compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Vet this skillatilladeniz/Kubeli3872 repos~1.6kAutomated safety check: PassMIT
Dstack Presetsdstackai/dstack2.3k—~403Automated safety check: PassMPL-2.0
Alibabacloud Ecs Sec Userspacealiyun/alibabacloud-ecs-troubleshoot-skills148—~2.6kAutomated safety check: NotesApache-2.0
Toolsastronomer/astronomer491—~1.1kAutomated safety check: PassCustom licence
Dstackdstackai/dstack2.3k—~6.2kAutomated safety check: WarnMPL-2.0
Asdfjjmartres/opencode133—~2.1kAutomated safety check: NotesMIT

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Categories

Questions about Vet

What does Vet do?

Run vet immediately after ANY logical unit of code changes. An agent skill from atilladeniz/Kubeli. Vet is an agent skill from atilladeniz/Kubeli. Run vet immediately after ANY logical unit of code changes.

When should I use Vet?

Vet fits situations like: tasks that involve Container orchestration.

How do I install Vet in Claude Code?

Run `npx skills add atilladeniz/Kubeli --skill vet -a claude-code`. Or copy the skill folder (.claude/skills/vet in atilladeniz/Kubeli) into .claude/skills/vet in your project. Claude Code loads it when a task matches its description.

How do I install Vet in Codex?

Run `npx skills add atilladeniz/Kubeli --skill vet -a codex`. Or copy the skill folder (.claude/skills/vet in atilladeniz/Kubeli) into .agents/skills/vet in your project. Codex loads it when a task matches its description.

Can I use Vet 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 atilladeniz/Kubeli --skill vet -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vet, .gemini/skills/vet, .github/skills/vet and .opencode/skills/vet in your project.

What does Vet need to run?

Going by SKILL.md and its folder, Vet needs Python for the scripts in its folder and the command-line tools its instructions call (pip, pipx, uv and opencode). Our summary lists: Python 3.

Does Vet access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Vet 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Vet use?

Vet is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Vet use?

About 1.6k tokens (SKILL.md is roughly 6.4k 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 Vet?

Skills that share tags, products or a category with Vet: Dstack Presets (dstackai/dstack, 2.3k stars), Alibabacloud Ecs Sec Userspace (aliyun/alibabacloud-ecs-troubleshoot-skills, 148 stars), Tools (astronomer/astronomer, 491 stars) and Dstack (dstackai/dstack, 2.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vet?

atilladeniz (a GitHub user) maintains it in atilladeniz/Kubeli, which has 387 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 5, 2026.

Source: atilladeniz/Kubeli on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.