# Set Up Workflow: Structuring a Claude Code Project for Automated End-to-End Workflows
## What This Is
This guide outlines a professional project structure for building AI agents that automate complex business processes. By organizing instructions and connections in a specific folder layout, developers can create persistent systems that review code, manage project tickets, and integrate with external tools without manual intervention.
Source: https://uxdesign.cc/designing-with-claude-code-and-codex-cli-building-ai-driven-workflows-powered-by-code-connect-ui-f10c136ec11f
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://uxdesign.cc/designing-with-claude-code-and-codex-cli-building-ai-driven-workflows-powered-by-code-connect-ui-f10c136ec11f) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Workflow** — an end-to-end automation pattern or integration pipeline.
- Study the workflow architecture from the source and context below
- Identify which parts I can implement locally vs. parts that need external services
- For local parts: implement them using my existing stack and API keys
- For external parts: tell me exactly what services I need and help me configure the integration code
- Wire up any required API calls using keys from my .env files
## Additional Context
- Scan the user's current project root and create the standard Claude Code directory structure: `.claude/agents/`, `.claude/commands/`, `.claude/hooks/`, and `.claude/skills/` if they do not already exist.
- Create a `CLAUDE.md` file in the project root. Populate it with a high-level project summary, key architectural decisions, and an inventory of the main technologies used to provide foundational context for the agent.
- Generate a boilerplate `code-reviewer.md` agent file within the `.claude/agents/` directory. Define its purpose to review code quality and include a placeholder checklist for common issues like missing error handling, use of 'any' types, and lack of tests, specifying the use of the Opus model for analysis.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it