# Set Up Workflow: Production-Grade Claude Code Workflow with Project Memory and Automation
## What This Is
This outlines an advanced workflow for automating software development using Claude Code, based on a real-world project showcase. It establishes a central 'project memory' using CLAUDE.md files and integrates custom commands and GitHub Actions to automate coding tasks, ensure quality, and accelerate development cycles.
Source: https://dev.to/yooi/building-with-ai-my-still-evolving-workflow-with-claude-code-365b
## 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://dev.to/yooi/building-with-ai-my-still-evolving-workflow-with-claude-code-365b) 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
- Clone the 'claude-code-showcase' repository from github.com/ChrisWiles/claude-code-showcase to a local directory to access the workflow templates and examples.
- Scan the user's current project directory and copy the example `.claude/commands` folder, `.github/workflows` files, and the root `CLAUDE.md` template from the showcase repository into the user's project, adapting paths and configurations as needed.
- Execute an initial analysis of the user's codebase to populate the `CLAUDE.md` file with the project's tech stack, coding standards, key file locations, and important scripts, thereby establishing the initial project memory.
## 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