Cli Configuration Deployment

Organization

VectorSpaceLab

cli-configuration-deployment

Use ADK CLI commands, agent/app discovery, YAML configuration, local run/web/API server flows, eval/test commands, deployment commands, and safe CLI/config inspection.

Overview

Publisher	VectorSpaceLab
Repository	`AREX-Skill`
Skill name	`cli-configuration-deployment`
Stars	341
Forks	26
Bundled files	4
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by VectorSpaceLab on GitHub. Read the source before you install it.

Installation

Install the Cli Configuration Deployment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adk-python/sub-skills/cli-configuration-deployment
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adk-python/sub-skills/cli-configuration-deployment

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/skills/repositories/repo-skills/adk-python/sub-skills/cli-configuration-deployment .claude/skills/cli-configuration-deployment

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cli Configuration Deployment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cli Configuration Deployment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cli Configuration Deployment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ADK CLI, Configuration, and Deployment

Use this sub-skill when a user asks how to run or expose an ADK app with the adk console script, why an agent is not discovered by adk web, how to structure a Python or YAML agent app, how to validate generated YAML config, or how to reason about adk deploy targets and credentials. ADK 2.3.0 provides the google.adk import root and the adk console command on Python 3.10+.

Route Common Requests

Run locally: Use CLI reference for adk run, adk web, adk api_server, adk test, adk eval, adk eval_set, and safe --help checks.
Fix app discovery: Use configuration for loader conventions, root_agent/app, agent.py, __init__.py, root_agent.yaml, .env, and service registration.
Generate or validate YAML: Use configuration for AgentConfig.json, agent_class, sub_agents, tools, callbacks, CodeConfig, and schema-validation workflow.
Deploy safely: Use CLI reference for adk deploy cloud_run, adk deploy agent_engine, and adk deploy gke; use troubleshooting for credentials, project/region, optional extras, and generated source caveats.
Diagnose failures: Use troubleshooting for missing agents in web UI, server/log/CORS issues, YAML schema errors, deployment failures, missing optional dependencies, and model credential problems.
Inspect an installation: Run inspect_adk_cli.py to list available CLI commands and locate installed config schema metadata without starting servers, deployments, network calls, or credential flows.

Boundaries

Included here: adk console commands; agent loader and app discovery; root_agent/app; YAML config and schema metadata; local run, web, api_server, test, eval, and eval_set; deploy command families; service registry; graph visualization; safe CLI help diagnostics.
Route elsewhere: Eval assertion design and detailed metrics go to evaluation-debugging; Python Agent/LlmAgent object design goes to agent-construction; repository maintainer commands, schema regeneration PR workflow, and source test policy go to repo-development.

Safe Defaults

Prefer adk --help and subcommand --help for non-mutating CLI checks.
Do not launch adk web or adk api_server unless the user explicitly wants a local server.
Do not run adk deploy ... without confirmed project/region/credentials and a clear deployment target.
Treat base installs as intentionally minimal: optional DB, extensions, MCP, eval, GCP/cloud, and A2A integrations can be absent and should be diagnosed rather than assumed.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cli Configuration Deployment AI skill do?

Use ADK CLI commands, agent/app discovery, YAML configuration, local run/web/API server flows, eval/test commands, deployment commands, and safe CLI/config inspection.

Why use Cli Configuration Deployment on TypingMind?

Because you install it once and use it with any model. Cli Configuration Deployment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cli Configuration Deployment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adk-python/sub-skills/cli-configuration-deployment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cli Configuration Deployment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cli Configuration Deployment?

As many as you like. As long as a model supports skills, you can use Cli Configuration Deployment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cli Configuration Deployment AI skill free?

Yes. It is published on GitHub by VectorSpaceLab under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

ADK CLI, Configuration, and Deployment

Route Common Requests

Boundaries

Safe Defaults

Bundled files

Frequently asked questions

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