up-cli 0.1.0

AI-powered project scaffolding with docs, learn, and product-loop systems

up-cli

An AI-powered CLI tool for scaffolding projects with built-in documentation, learning systems, and product-loop workflows designed for use with Claude Code and Cursor AI.

Learned from real practice - Built on insights from 5+ billion tokens of development experience and commercial products. Extracts best practices from chat history, documentation patterns, and proven workflows.

Installation

pip install -e .

Quick Start

# Create new project
up new my-project

# Or initialize in existing project
cd existing-project
up init

Commands

Command	Description
up new <name>	Create a new project with full scaffolding
up new <name> --template <type>	Create project from specific template
up init	Initialize up systems in current directory
up init --ai claude	Initialize for Claude Code only
up init --ai cursor	Initialize for Cursor AI only
up init --systems docs,learn	Initialize specific systems only

Usage Examples

Create a new project

# Create a new project with all systems
up new my-saas-app

# Create with a specific template
up new my-api --template fastapi

Initialize in existing project

cd my-existing-project

# Full initialization
up init

# Claude Code focused setup
up init --ai claude

# Only add docs and learn systems
up init --systems docs,learn

Using the Learn System

# Auto-analyze your project and generate insights
/learn auto

# Research a specific topic with web sources
/learn research "authentication patterns"

# Generate a PRD from your codebase
/learn plan

Using the Product Loop

# Start autonomous development loop
./skills/product-loop/start-autonomous.sh

# Run with circuit breaker protection
./skills/product-loop/ralph_hybrid.sh

Systems

1. Docs System

docs/roadmap/vision/      # Product vision
docs/roadmap/phases/      # Phase roadmaps
docs/changelog/           # Progress tracking

2. Learn System

• /learn auto - Auto-analyze project
• /learn research [topic] - Research topic
• /learn plan - Generate PRD

3. Product Loop (SESRC)

• Circuit breaker (max 3 failures)
• Checkpoint/rollback
• Health checks
• Budget limits

Design Principles & Practices

AI-First Development

Design for AI collaboration, not just human readability.

• Context-aware scaffolding - Project structures optimized for AI agents to navigate and understand quickly
• Explicit over implicit - Clear file naming, directory structures, and documentation that AI can parse without ambiguity
• Prompt-friendly patterns - Code and docs written to be easily referenced in AI conversations
• Tool integration - Native support for Claude Code skills and Cursor AI rules

Documentation-Driven Development

Documentation is the source of truth, not an afterthought.

• Docs-first workflow - Write documentation before implementation to clarify intent
• Living documentation - Docs evolve with the codebase through automated learning systems
• Knowledge extraction - /learn commands analyze patterns and generate insights from real usage
• Structured knowledge - Vision, roadmaps, and changelogs in predictable locations for AI and human consumption

Product Loop Patterns (SESRC)

Autonomous development with safety guardrails.

• Circuit breaker protection - Max 3 consecutive failures before stopping to prevent runaway loops
• Checkpoint/rollback - Save state before risky operations, restore on failure
• Health checks - Validate system state between iterations
• Budget limits - Token and time constraints to prevent unbounded execution
• Human-in-the-loop - Critical decisions require explicit approval

Core Practices

Practice	Description
Incremental delivery	Ship small, working increments over big-bang releases
Fail fast, recover faster	Detect issues early, rollback automatically
Observable by default	Logging, metrics, and state visible to both AI and humans
Convention over configuration	Sensible defaults that work out of the box

Development

pip install -e .
pytest

License

MIT