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🎥 The Claude Code WORKFLOW with GitHub to Build Complex Apps

👤 Channel: Greg + Code
⏱️ Duration: 18:40
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📚 Video Chapters (10 chapters):

• Overview of the AI Coding workflow - 0:00
• Software Development Life Cycle - 1:39
• Creating and Refining GitHub Issues - 3:01
• Setting Up Your Foundation - 5:54
• Plan: Custom Slash Commands - 7:10
• Create, Test, Deploy - 8:59
• Your job vs. AI's job - 13:29
• Context Management with /clear - 14:32
• Claude via GitHub Actions - 15:07
• Work Trees: Run Parallel Agents - 16:28

Overview

This video provides a detailed walkthrough of an efficient AI-assisted coding
workflow using Claude Code and GitHub. The presenter shares their experiences,
insights, and practical steps for setting up a collaborative development
environment where AI agents assist in planning, coding, testing, and deploying
web applications. The workflow leverages established software development best
practices and tools to maximize productivity and code quality when working with
AI assistants.

Main Topics Covered

• High-level overview of the AI coding workflow with Claude Code and GitHub
• The role of traditional software development life cycle (SDLC) phases in AI-assisted coding
• Creating, refining, and managing GitHub issues for AI agents
• Setting up foundational tools: test suites, continuous integration (CI), and Puppeteer
• Designing and iterating on custom slash commands for workflow automation
• Human involvement vs. AI delegation at different workflow stages
• Context management and best practices for prompt engineering
• Using Claude via GitHub Actions and considerations around billing and efficiency
• Introduction and challenges with Git work trees for parallel agent workflows

Key Takeaways & Insights

• AI coding agents excel within structured workflows: Applying proven software development cycles (plan, create, test, deploy) helps maximize AI efficiency and reliability.
• Granularity and clarity in issues are critical: The more atomic and specific the GitHub issues, the better the results from AI agents, reducing ambiguity and rework.
• Human oversight is essential: While AI can automate much of the coding, humans should stay deeply involved in the planning and review stages to ensure code quality and alignment with requirements.
• Tests and CI/CD are non-negotiable foundations: A robust test suite and automated CI pipeline are crucial for safely iterating and relying on AI for development tasks.
• Prompt engineering (slash commands) is a core skill: Iteratively refining custom commands for Claude Code enables tailored agent behavior and better results.
• Context management boosts efficiency: Regularly clearing the AI’s context ensures each issue is handled independently, improving performance and reducing token consumption.
• Parallel agent workflows (work trees) are promising but can be cumbersome for small projects: While potentially powerful, work trees may introduce more overhead than benefit for early-stage or linear projects.

Actionable Strategies

1. Start with Comprehensive Planning:
2. Use tools like Super Whisper for dictation and collaborate with AI to turn ideas into detailed requirements and granular GitHub issues.
3. Install Essential Tooling:
4. Set up the GitHub CLI for seamless agent-repo interaction.
5. Establish a test suite and configure continuous integration (e.g., GitHub Actions) from the outset.
6. Integrate Puppeteer for automated UI testing.
7. Refine Issues Before Coding:
8. Spend time making each issue as specific and atomic as possible to set AI agents up for success.
9. Leverage Custom Slash Commands:
10. Develop and iterate on slash commands to automate planning, coding, testing, and PR review steps.
11. Encourage “think harder” or similar prompts to push the AI for deeper reasoning and better plans.
12. Maintain Human Oversight:
13. Personally review PRs or set up AI-based reviews with specialized slash commands (e.g., mimicking expert review styles).
14. Decide which stages to delegate and which to supervise closely.
15. Clear AI Context Regularly:
16. Use commands like /clear to reset context after each issue, ensuring independent task execution.
17. Be Strategic With GitHub Actions and Work Trees:
18. Use GitHub Actions for small changes; avoid for large, complex tasks due to cost and context limitations.
19. Only use work trees when parallel development is truly beneficial, and be prepared for extra overhead.

Specific Details & Examples

• Workflow Structure: Plan (break down issues), Create (write code), Test (run suite and UI tests), Deploy (merge & deploy).
• First Steps: Initial requirements were created via dictation and refined with Claude into a requirements document and then into ~30–40 GitHub issues.
• Tech Stack Choices: Rails was favored for its modularity and integrated testing, though the presenter has a Python background.
• Testing Tools: Continuous integration is set up through GitHub Actions; Puppeteer is used for UI testing.
• Slash Commands: Placed in thecloud/commands; arguments (like issue numbers) are passed in; planning phase uses “think harder” prompts and scratchpads for reasoning.
• Review Process: Separate slash commands are used to have Claude review PRs in the style of expert developers (e.g., Sandy Metz).
• Context Management: /clear is run after merging PRs to reset Claude’s working memory; issues are written to be self-contained.
• Billing Note: Using Claude via GitHub Actions incurs extra API charges even for Max plan users, making console use preferable for frequent/big tasks.
• Work Trees: Allow multiple Claude instances to work on separate branches in parallel by creating multiple repo subdirectories, but introduce permission and merge conflict overhead.

Warnings & Common Mistakes

• Too broad or vague issues lead to poor AI performance: Atomic, well-defined issues are essential.
• Assuming AI can handle everything autonomously: Human review and planning remain critical, especially for complex or ambiguous requirements.
• Neglecting tests and CI: Without automated testing, AI-generated changes can easily introduce regressions.
• Overusing work trees: For projects requiring mostly linear development, work trees can be more trouble than they’re worth due to repetitive setup and permissions management.
• Relying exclusively on AI for code review: Balance automated reviews with personal oversight to catch subtle issues or maintain coding standards.

Resources & Next Steps

• Tools to Set Up:
• GitHub CLI (for agent-repo interaction)
• CI/CD pipelines (GitHub Actions)
• Puppeteer (for UI testing)
• Recommended Reading:
• “All of My AI Skeptic Friends Are Nuts” by Thomas Tacic (linked in video description)
• Anthropic’s best practices for agent coding (especially posts by Boris, creator of Claude Code)
• Further Learning:
• Watch the presenter’s other video: “Claude Code Pro Tips” for advanced tips.
• Suggested Improvements:
• Continue refining slash commands and prompts for better agent performance.
• Experiment with parallelization only as team/project complexity grows.
• Evaluate and balance billing considerations when using Claude via API or GitHub Actions.

Summary: The video presents a proven, practical workflow for leveraging AI
agents in software development, emphasizing the importance of clear planning,
robust tooling, and thoughtful human-AI collaboration to achieve rapid,
high-quality results.