Source: Dev.to

This is a submission for the GitHub Copilot CLI Challenge

🚀 From Cyberpunk to Zen: A Trilogy of TUI Tools

For this challenge I didn’t just build one tool; I built a trilogy of Terminal User Interface (TUI) applications.
My goal was to leverage GitHub Copilot to transform the cold command line into a living, breathing digital ecosystem.

The journey evolved from a rigid, agent‑based approach to a fluid, context‑engineered workflow. Below is the result of that evolution.

---

1️⃣ The Netrunner Deck (Cyberpunk Monitor)

The “High‑Friction” Experiment

A cyberpunk‑themed network monitor that visualizes Docker containers and network connections as an ASCII topology.

• Matrix Rain effects on the side panels.
• Deterministic Placement – nodes are positioned using SHA‑256 hashing so your infrastructure always looks the same.
• Live Monitoring – real‑time Docker and network status updates.

---

2️⃣ Code Bonsai (Git Visualizer)

The “Freestyle” Experiment

A digital terrarium that transforms your Git repository into a growing ASCII bonsai tree:

• Trunk – thickness represents lines of code.
• Leaves – represent healthy files.
• Weather – the “sky” changes based on commit activity (sunny for active days, moonlit for dormant ones).
• Wildlife – “bugs” appear on branches proportional to linting errors.

---

3️⃣ Cozy Cabin (Terminal Wallpaper)

The “Context Engineering” Masterpiece

An interactive terminal background designed for focus and calm. It renders a “digital cabin window” with:

• Procedural Scenery – layered mountains and forests with live weather from OpenWeatherMap (clouds, rain, day/night cycles).
• System Shelf – a quirky take on system monitoring:
  • 🔋 Battery Cat – naps when discharging, wakes up when plugged in.
  • 🌿 RAM Plant – wilts if memory usage gets too high.
  • 🔥 CPU Fireplace – flickers more intensely as your CPU load increases.

---

🏗️ Architecture & Workflow

The most interesting part of this challenge wasn’t just the code, but how my interaction with Copilot changed. I moved from using pre‑defined agents to a Context Engineering approach.

┌──────────────────────┐      ┌──────────────────────┐      ┌──────────────────────┐
│  Phase 1: Netrunner  │      │  Phase 2: Code Bonsai│      │  Phase 3: Cozy Cabin │
│  (Rigid Agents)      │      │  (Freestyle/Native) │      │  (Context First)     │
└──────────┬───────────┘      └──────────┬───────────┘      └──────────┬───────────┘
           │                             │                             │
           ▼                             ▼                             ▼
   [Defined Persona]            [Raw Prompts + Models]        [Plan.md + Context]
           │                             │                             │
           ▼                             ▼                             ▼
    (Slow Iteration)             (Fast but Chaotic)           (Rapid & Reliable)
           │                             │                             │
           ▼                             ▼                             ▼
    "It works, but…"            "80% There"                 "90% Ready Code"

---

🧰 The Tech Stack

---

🎥 Demo

• The Netrunner Deck – Repository: – Demo:
• Code Bonsai – Repository: – Demo:
• Cozy Cabin – Repository: – Demo:

---

My Experience with GitHub Copilot CLI

Phase 1 – “By the Book” Approach

• Time Cost: 2 days
• Project: The Netrunner Deck
• Model(s): Claude Opus 4.5 (planning & coding) → switched to Sonnet to conserve premium requests.
• Takeaway: The workflow was slow; managing agents and rigid context consumed more time than actual coding.

Phase 2 – “Freestyle” Approach

• Time Cost: 1 day
• Project: Code Bonsai
• Model(s): Sonnet (planning) + Codex (implementation)
• Takeaway: Much faster. The CLI returned ~80 % of the desired code, but occasionally missed architectural nuance.

Phase 3 – “Context Engineer” Approach

• Time Cost: 1 day
• Project: Cozy Cabin
• Model(s): (not explicitly listed – focus on curated context)
• Takeaway: The sweet spot. A lightweight setup combined with deliberate Plan Review and Context Engineering yielded rapid, reliable results.

---

End of submission.

# Generate Code

**Model Usage**: I used **Codex** exclusively, relying on my engineered context to guide it.

**The Result**: This was the peak experience. The output adapted to **90 %** of my expectations. By focusing on the *plan* rather than the *persona*, Copilot became a true extension of my thought process.

---

## Conclusion: The “Greenfield” Flow

Based on this trilogy of experiments, here is my recommended workflow for starting a new project with Copilot CLI:

1. **Plan with Power**  
   Use high‑reasoning models like **Claude Opus** or **Gemini Pro** to create the initial plan.  

2. **Review**  
   Review the plan carefully. *Note: The plan is saved in the chat session memory by default, not as a file in the repo, to keep the workspace clean.*

3. **Code with Speed**  
   Switch to **GPT Codex** for implementation. It’s faster and follows the context well.

4. **Feedback Loop**  
   Implement the code, check the layout/structure, and provide feedback.

5. **Initialize Context**  
   Use `/init` to generate project‑specific Copilot instructions.

6. **Optimized Instructions**  
   Use the [awesome‑copilot](https://github.com/github/awesome-copilot) plugin to find the best agent/instruction/prompt for your specific project type.  

   *Iterate*: Return to the planning step for the next feature. **Crucial**: Update the Copilot instructions frequently to keep the context fresh.

7. **Leverage MCP**  
   Use Model Context Protocol (MCP) tools like **Context7** (to check best practices & modern frameworks) and **Serena** (to optimize context indexing).

The Copilot CLI isn’t just a code generator; it’s a collaboration engine. My key takeaway is that **Plan > Config**. A well‑structured plan and curated context are far more effective than a heavily configured environment.

---

## 📚 References

- **Tools**:  
  - [GitHub Copilot CLI](https://github.com/features/copilot/cli)  
  - [Context7](https://context7.com/) (MCP)  
  - [Serena](https://github.com/oraios/serena) (MCP)

- **Inspiration**:  
  - [awesome‑copilot](https://github.com/github/awesome-copilot) (prompt engineering)  
  - [Copilot best practice](https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-best-practices)

- **Libraries**:  
  - [Textualize](https://textualize.io/)