Power-Up #4: My AI Workflow

In the previous post, I shared about OpenCode works. Now, let’s go one level deeper. This is the exact workflow I use daily to ship code faster, combining GitHub issue, Copilot Agent, and multiple AI reviewers into a single pipeline.

The Big Idea

AI is not a single tool. It’s a system of agents working together:

• One writes code

• Others review

• Others validate

• You orchestrate

Step 1 — Start with a Real Issue

Every task begins with a clearly defined issue:

👉 https://github.com/jellydn/my-ai-tools/issues/174

This is critical because:

• AI needs context

• Reviews need scope

• You need a single source of truth

A vague issue = weak AI output.

Step 2 — Let Copilot Do the First Draft

Instead of writing code from scratch, I assign the issue to Copilot then in will open PR and work on it:

👉 https://github.com/jellydn/my-ai-tools/pull/175

At this stage:

• Copilot generates the implementation

• I review direction, not syntax

Step 3 — AI Code Review with CodeRabbit

Before touching anything manually, I run an AI review.

CodeRabbit helps:

• Catch bugs early

• Identify bad patterns

• Suggest improvements

This is your first quality gate.

Step 4 — Pull PR Locally

Next, I check out the PR locally using:

👉 https://github.com/tobi/try

This makes it trivial to clone repository and manage under src/tries folder:

• Checkout PRs

• Run the code

• Validate behavior

No friction = more testing.

Step 5 — Multi-Agent Review (This Is the Game Changer)

Now comes the most powerful part. I don’t rely on a single AI. I run multiple agents:

• Codex

• OpenCode

• Pi

• Claude Code

Each one:

• Thinks differently

• Finds different issues

• Suggests different improvements

This creates a diverse review system, similar to having multiple senior engineers.

Final Thoughts

This workflow helps me:

• Ship faster

• Maintain high quality

• Reduce mental load

The key is not better prompts. It’s better orchestration.