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Project Scaffold

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ojiudezue
project-scaffold

Scaffolds any new software project with full product-layer AND code-layer infrastructure in one pass. Use this skill whenever the user says "scaffold a project", "new project", "start a project", "set up a new app", "spin up a project", "create a new repo", or any variation of initiating a fresh software build. Also trigger when the user mentions wanting a product brief, market research, CI/CD setup, testing infrastructure, or prototyping system as part of starting something new. This skill does NOT just create folder structures — it applies product-design-engineering practice as a co-equal part of scaffolding. If the user is starting something from scratch, use this skill.

Overview

Publisherojiudezue
Repositoryproductmind-skills
Skill nameproject-scaffold
Stars
81
Forks
18
Bundled files
6
Links
  • 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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ojiudezue on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ojiudezue/productmind-skills/tree/main/.claude/skills/new-project-scaffolding
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/ojiudezue/productmind-skills.git /tmp/productmind-skills
mkdir -p .claude/skills
cp -r /tmp/productmind-skills/.claude/skills/new-project-scaffolding .claude/skills/project-scaffold
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Project Scaffold 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 Scaffold 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 Scaffold 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 Scaffold System

Scaffolds a complete software project across two layers simultaneously:

  • Project layer: product brief, market research, viability gate, quality tracking
  • Code layer: repo structure, testing, CI/CD, documentation, prototyping

Workflow

Step 1: Gather Context (ask only what you can't infer)

Ask the user three things:

  1. Project type — What are we building?

    • iOS app, Android app, web app (React/Next/etc.), API service, CLI tool, Python package, monorepo, etc.
    • If ambiguous, propose your best guess and let them correct it.
  2. Reference projects — Which existing projects should we borrow patterns from?

    • This saves enormous time. Reuse folder conventions, CI configs, testing setups, CLAUDE.md patterns.
    • If the user has no references, proceed from the templates in templates/.
  3. Domain context — What does this product do, who is it for, and what problem does it solve?

    • One or two sentences is enough. This feeds the product brief and viability check.

Step 2: Run the Viability Gate

Before writing any code, generate a viability assessment. Read references/viability-criteria.md for the full rubric.

The viability assessment covers:

  • Problem clarity and urgency
  • Target user definition
  • Competitive landscape (quick scan)
  • Differentiation / unfair advantage
  • Technical feasibility at current resources
  • Revenue or impact path

Kill criteria: If 3+ of the 6 dimensions score "Weak," flag the project as high-risk and recommend the user either pivot the concept or consciously accept the risk before proceeding. Do not silently continue.

Write the assessment to docs/viability-assessment.md.

Step 3: Generate the Product Brief

Create docs/product-brief.md with this structure:

# [Project Name] — Product Brief

## Problem
## Target User
## Value Proposition
## Success Criteria (measurable)
## Scope Boundaries (what this is NOT)
## Key Assumptions
## Open Questions

Step 4: Conduct Market Research

Create docs/market-research.md. Read references/market-research-template.md for depth guidance.

This is not a placeholder — do real research:

  • Direct competitors (3-5 minimum)
  • Adjacent products that partially solve the problem
  • Differentiation gaps and opportunities
  • Pricing/business model patterns in the space
  • User sentiment signals (app store reviews, forum complaints, Reddit threads)

Use web search to populate this. If web search is unavailable, clearly mark sections as "NEEDS RESEARCH" so the user knows to fill them.

Step 5: Generate CLAUDE.md

Create a CLAUDE.md tailored to the project type. Read references/claude-md-patterns.md for type-specific templates.

Every CLAUDE.md must include:

  • Project description and architecture overview
  • Tech stack and key dependencies
  • Code conventions (naming, file organization, import style)
  • Testing expectations (what to test, how to run)
  • PR/commit conventions
  • Common pitfalls specific to this stack

Step 6: Scaffold the Folder Structure

project-root/
├── CLAUDE.md
├── README.md
├── docs/
│   ├── product-brief.md
│   ├── market-research.md
│   ├── viability-assessment.md
│   ├── quality-log.md          # Bug classification tracker
│   └── milestones/             # README per milestone
│       └── m0-scaffold.md
├── prototypes/                 # HTML/CSS prototypes (UI projects only)
│   └── README.md
├── src/                        # Varies by project type
├── tests/                      # Mirrors src/ structure
├── .github/
│   └── workflows/
│       └── ci.yml
└── .gitignore

For the quality-log.md, initialize it with this structure:

markdown
# Quality Log

## Bug Classification
| ID | Date | Severity | Category | Description | Root Cause | Resolution | Recurrence? |
|----|------|----------|----------|-------------|------------|------------|-------------|

## Categories
- Logic, UI/UX, Performance, Data, Security, Integration, Infrastructure

## Patterns
(Updated as bugs accumulate — look for recurring root causes)

Step 7: Set Up Testing Infrastructure

Do not set up just one type of test. Read references/testing-by-project-type.md and set up what's appropriate:

Project TypeTesting Layers
Web appUnit (vitest/jest), component (testing-library), e2e (playwright), accessibility (axe)
iOSUnit (XCTest), UI (XCUITest), snapshot
APIUnit, integration, contract, load
CLIUnit, integration (bats/shellspec), snapshot
Python packageUnit (pytest), type checking (mypy), property-based (hypothesis)

Create config files, sample test files, and npm/pip scripts so tests run immediately.

Step 8: Set Up CI/CD

Generate .github/workflows/ci.yml (or equivalent) that:

  • Runs all test types from Step 7
  • Runs linting/formatting checks
  • Runs type checking if applicable
  • Fails fast on the cheapest checks first
  • Includes a build/deploy step (even if it's just a placeholder)

Read references/ci-templates.md for project-type-specific pipelines.

Step 9: Set Up Prototyping (UI Projects Only)

If the project has a user interface:

  1. Create prototypes/ with a README explaining the prototyping workflow
  2. Set up a simple HTML/CSS prototype scaffold (no framework, pure markup)
  3. The rule: prototype in HTML/CSS first, then build in the target framework
  4. Prototypes are iterated and reviewed before production code begins

Step 10: Fetch Skills

Check references/skills-by-project-type.md and recommend or fetch relevant skills across these categories:

CategoryPurpose
DesignUI design, interaction design, information architecture, accessibility
TestingUnit, integration, e2e, performance — multiple, not just one
ProductStrategy, analytics instrumentation, user research, prioritization
PlatformiOS, Android, web, API, CLI — whatever the project needs

List what skills are available and which ones you're applying. If a needed skill doesn't exist, note it as a gap.

Step 11: Write the Milestone 0 Doc

Create docs/milestones/m0-scaffold.md documenting:

  • What was set up and why
  • Architecture decisions made
  • Reference projects borrowed from
  • What the next milestone should tackle

This is the first entry in the project's documentation trail. Every future milestone gets its own doc.


Key Principles

  • Product-design-engineering are co-equal from day one. Scaffolding is not just folder creation — it's establishing how the team thinks about the product.
  • Kill early. The viability gate exists so bad ideas don't consume weeks of effort.
  • Multiple skills per category. One testing framework is not enough. One design perspective is not enough.
  • Documentation is continuous. Every milestone, every code critique, every bug gets recorded.
  • Borrow aggressively. Reference projects exist so the system doesn't reinvent conventions every time.

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 Scaffold AI skill do?

Scaffolds any new software project with full product-layer AND code-layer infrastructure in one pass. Use this skill whenever the user says "scaffold a project", "new project", "start a project", "set up a new app", "spin up a project", "create a new repo", or any variation of initiating a fresh software build. Also trigger when the user mentions wanting a product brief, market research, CI/CD setup, testing infrastructure, or prototyping system as part of starting something new. This skill does NOT just create folder structures — it applies product-design-engineering practice as a co-equal...

Why use Project Scaffold on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ojiudezue/productmind-skills/tree/main/.claude/skills/new-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 Scaffold?

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 Scaffold?

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

Is the Project Scaffold AI skill free?

It is published on GitHub by ojiudezue. Check the repository for licensing terms. 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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