Project Scaffolding

Organization

VectorSpaceLab

project-scaffolding

Guides creation and discovery of new Agent Starter Pack projects from built-in, local, or remote templates.

Overview

Publisher	VectorSpaceLab
Repository	`AREX-Skill`
Skill name	`project-scaffolding`
Stars	341
Forks	26
Bundled files	3
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.
3 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 Project Scaffolding 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/agent-starter-pack/sub-skills/project-scaffolding
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/agent-starter-pack/sub-skills/project-scaffolding

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/agent-starter-pack/sub-skills/project-scaffolding .claude/skills/project-scaffolding

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

Use it in TypingMind

Enable Project Scaffolding 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 Project Scaffolding 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 Project Scaffolding 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.

Project scaffolding

Use this sub-skill when the user wants to start a new project from Agent Starter Pack or explore which template to use.

Covers

create for new projects from built-in, local, or remote templates.
list for browsing built-in templates, the official ADK samples, or a local/remote template source.
Template choice, agent selection, and generation-time options such as deployment target, datastore, session type, agent directory, --prototype, --in-folder, --auto-approve, --skip-checks, --base-template, --bq-analytics, and --google-api-key.
Remote-template syntax, version locking, and base-template overrides.
Generated-project shape: template-driven README.md, Makefile, agent directory, optional CI/CD scaffolding, and optional data-ingestion/session wiring.

Excludes

In-place project edits, extraction, and upgrades belong in project-maintenance.
CI/CD provisioning, deployment, and Gemini Enterprise registration belong in deployment-ops.

Read first

../../references/cli-reference.md for command routing.
../../references/template-catalog.md for the built-in template map.
../../references/package-overview.md for install and sanity-check guidance.
references/workflows.md for the end-to-end creation flow.
references/remote-templates.md for local@, adk@, and remote repository syntax.
references/troubleshooting.md for template-selection and creation failures.

Common workflow

Confirm the package is installed with the bundled sanity checker when needed.
Use list when the user is still choosing a template.
Use create for new project generation.
If the user provides a remote template, read the remote-template reference before selecting --base-template or assuming the template is local.
After generation, hand off deployment or maintenance questions to the owning sub-skill instead of expanding this one.

Useful signals

Built-in templates: adk, adk_a2a, adk_live, agentic_rag, langgraph, adk_go, adk_java, adk_ts.
Remote-template cues: local@, adk@, adk-py@, GitHub shorthand, or a full Git URL.
Project-shape cues: --agent-directory, --session-type, --datastore, --prototype, --in-folder, and --base-template.
Runtime constraints: project name length, agent-directory validity, template availability, and version-lock compatibility.

Validation mindset

Prefer a read-only install sanity check and command discovery before a real generation run.
Treat remote-template fetches as optional or networked work, not as the default verification path.
Do not route maintenance or cloud-deployment troubleshooting back here unless the issue is truly template selection or generation-time configuration.

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 Project Scaffolding AI skill do?

Guides creation and discovery of new Agent Starter Pack projects from built-in, local, or remote templates.

Why use Project Scaffolding on TypingMind?

Because you install it once and use it with any model. Project Scaffolding 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 Project Scaffolding in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/agent-starter-pack/sub-skills/project-scaffolding. 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 Project Scaffolding?

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 Project Scaffolding?

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

Is the Project Scaffolding 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

Project scaffolding

Covers

Excludes

Read first

Common workflow

Useful signals

Validation mindset

Bundled files

Frequently asked questions

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