How to use GitHub Copilot better than 99% of people Most developers accept the first suggestion and move on. Meanwhile, the top 1% are using Agent Mode, assigning issues to Copilot, and connecting external tools via MCP. I built a 12-tip visual carousel to close that gap. ━━━━━━━━━━━━━━━━━━━━━━ 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: 𝟭. Switch to Agent Mode 𝟮. Assign GitHub Issues directly to Copilot 𝟯. Add custom instructions to your repo 𝟰. Pick the right model for the task 𝟱. Create reusable prompt files 𝟲. Connect tools via MCP 𝟳. Use Copilot CLI in your terminal 𝟴. Master @workspace, @terminal, and slash commands 𝟵. Automate PR reviews with Copilot 𝟭𝟬. Build agent skills and extensions 𝟭𝟭. Configure org-level governance 𝟭𝟮. Treat your repo as Copilot's brain ━━━━━━━━━━━━━━━━━━━━━━ Every tip has real examples, terminal mockups, code snippets, and links to official GitHub Docs. No fluff. No "just use better prompts" advice. This is the reference I wish I had when I started. 📥 Save this for your next sprint. ♻️ Repost if your team needs this. #GitHubCopilot #AI #DeveloperProductivity #CopilotTips #GitHub #SoftwareEngineering #DevTools

🚀 Excited to share my latest project: DuplicateEliminatorPro.exe Just launched DuplicateEliminatorPro.exe on GitHub! This is a tool designed to help eliminate duplicate files efficiently. Whether you're managing a large storage system, organizing project files, or cleaning up your digital workspace, this utility makes it simple to identify and remove duplicates. 📁 What it does: Detects duplicate files across your system Provides a clean, professional interface Helps free up storage space Fast and reliable processing Identify files with different names but with same content Normal mode and Accelerated mode 🔗 Check it out and feel free to contribute, fork, or share feedback: 👉 https://lnkd.in/dwJ3fAK7 Would love to hear your thoughts and suggestions for improvements! #GitHub #OpenSource #WindowsTools #DeveloperTools #CodingProject #SoftwareDevelopment

Most developers think GitHub Copilot is underperforming. They're wrong. Their prompts are underperforming. After studying GitHub's official documentation and testing dozens of approaches, I've identified 7 core rules that completely transform the quality of Copilot's output. These aren't hacks or workarounds — they're fundamentals that most developers skip entirely. Here's a quick summary: - Start general, then get specific — give Copilot the goal before the constraints - Use examples — show expected inputs and outputs; unit tests work brilliantly here - Break down complex tasks — one focused step at a time beats one giant request - Eliminate ambiguity — "this function" beats "this" every single time - Control your context — open relevant files, close irrelevant ones - Iterate — treat it as a conversation, not a one-shot command - Reset your thread — stale chat history actively hurts response quality - And if you manage a team of developers, there's one more thing worth knowing: Prompt Files. This feature lets you save prompts as reusable .md files and commit them directly to your repository. Your entire team runs the same prompts, consistently. Code reviews, test generation, API documentation — all standardized. The developers getting the most value from AI coding tools aren't the ones with the best tools. They're the ones who've learned to communicate with them. This skill is becoming a genuine competitive advantage. Now is the time to build it. 🎥 I put together a full video walking through all 7 rules + a hands-on demo of Prompt Files. Link in the comments below.

Cheatsheet on GitHub Copilot CLI. 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱. 𝗢𝗻𝗲 𝗽𝗮𝗴𝗲. 𝗭𝗲𝗿𝗼 𝗳𝗹𝘂𝗳𝗳. Most developers use Copilot in the IDE. Fewer have explored Copilot CLI. putting together a single-page cheatsheet covering the full workflow → ━━━━━━━━━━━━━━━━━━━━━━ 𝟭. Getting Started & Authentication 𝟮. Custom Instructions — Copilot's persistent memory 𝟯. Instructions File Hierarchy (global → repo → path) 𝟰. CLI Best Practices that actually matter 𝟱. Project File Structure conventions 𝟲. Skills — the superpower most people skip 𝟳. Agent & Extension ideas 𝟴. MCP Server setup (built-in, custom, third-party) 𝟵. Permissions & Safety controls 𝟭𝟬. The 4-Layer Architecture 𝟭𝟭. Daily Workflow Pattern 𝟭𝟮. Quick Reference for all commands ━━━━━━━━━━━━━━━━━━━━━━ 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆? Copilot CLI isn't autocomplete in a terminal. When you layer these four together: ◈ 𝗟𝟭 — Custom Instructions ◈ 𝗟𝟮 — Skills ◈ 𝗟𝟯 — MCP Servers ◈ 𝗟𝟰 — Custom Agents ...it becomes a fully contextual coding partner that understands your project, your stack, and your conventions. ━━━━━━━━━━━━━━━━━━━━━━ 𝗠𝘆 𝗱𝗮𝗶𝗹𝘆 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄: cd project && copilot ↓ Shift+Tab → Plan Mode ↓ Describe feature intent ↓ Shift+Tab → Interactive ↓ /compact ↓ /diff → review changes ↓ Commit frequently ↓ New session per feature ━━━━━━━━━━━━━━━━━━━━━━ Grab the cheatsheet below ↓ Share it with your team. ♻️ 𝗥𝗲𝗽𝗼𝘀𝘁 if this is useful to your network. #GitHubCopilot #CopilotCLI #DeveloperProductivity #AI #DevTools #SoftwareEngineering #GitHub #CodingWorkflow

Mid stage enshitification of GitHub and GitHub Copilot: Ads in pull requests. They walked it back, but the advertisements were an example of what I posted about yesterday: Abdication of responsibility to exercise good judgement. To the Github Copilot team's credit they explicitly called out 'poor judgement' as the core issue and took some responsibility after the fact. If you take their stated goal at face value the team was trying to surface 'helpful tips' to users. How the defined a 'tip' and where they chose to surface them both lacked appropriate judgement. To a point Charles (Ed) Becze brought up yesterday, they met a specification (ads in pull requests) but not the requirements (helpful tips for users). Zack Manson (link below) connected the dots to Doctorow for me with this quote on enshitification: Here is how platforms die: first, they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves. Then, they die. Cory Doctorow https://lnkd.in/gbVNzhDM https://lnkd.in/gWvXS6VU

Queueing prompts in Cowork and GitHub Copilot may look like a small feature, but it noticed it has already had a major impact on my own work mode: From synchronous prompting -> asynchronous agent orchestration Until now, interacting with agents often meant for me: - waiting for completion before sending the next prompt - thus lots of context switching - ideas getting lost mid-process With queueing (in GitHub Copilot Agent Mode and Copilot Cowork), I notice a clear shift in my own workflow: - I can decouple thinking from the actual agentic execution - I externalize my backlog directly into the agent without having to wait - I move from “one task at a time” to managing parallel intent In practice: - I define the next steps while the agent is still running - I let the agent pull work instead of constantly pushing prompts - I stay in a planning mindset instead of monitoring execution This feels less like using a tool and more like coordinating work. - Agents are no longer just reactive assistants. - They start behaving like systems with backlog, state, and flow control. - I believe especially the task backlog will become increasingly important and a new field for innovation So in summary: Queueing is a step toward: - more persistent and autonomous agents - continuous execution - agent-driven prioritization Or simply: less context switching #CopilotCowork #GitHubCopilot

GitHub Copilot's custom instructions are powerful — but only when the right files are in context. If the file isn't open or referenced in the prompt, the custom instructions are not added to the context. Silent failure. Here's a 3-layer pattern I use to keep Copilot reliably context-aware: 𝗟𝗮𝘆𝗲𝗿 𝟭 — applyTo instructions (primary) Glob-pattern scoped. Fires automatically when matched files are in context. Zero overhead. 𝗟𝗮𝘆𝗲𝗿 𝟮 — Atomic agent skill per module (fallback) When files aren't in context, a skill with strong routing language loads the right instructions on demand. One skill = one module. 𝗟𝗮𝘆𝗲𝗿 𝟯 — Composite workflow skill (cross-cutting) Some prompts don't belong to a single module — they span a workflow. A single skill can load instructions for Orders + Shipping together, giving Copilot the full picture before it reasons. Key rule: name composite skills by workflow, not by module combination. ✅ order-fulfillment-workflow ❌ orders-and-shipping-skill Workflow names survive refactoring. Module combinations don't. Layers 2 and 3 still depend on Copilot's routing judgment. Use mandatory language in skill descriptions — "ALWAYS invoke... Do NOT answer from memory" — to reduce flakiness. Determinism only comes from pre-fetching context before the LLM sees the prompt. But for Copilot-native workflows, this gets you close. #GitHubCopilot #AITooling #SoftwareArchitecture #DeveloperExperience #SpecDrivenDevelopment

I've been using GitHub Copilot CLI extensively for the past few weeks. It's been good. I found something recently as part of the exploration that surprised me. I ran into a command called /chronicle improve. Copilot CLI went through my entire session history, every prompt I had typed, every correction I made when the agent got something wrong, every time I had to redirect it because it diverged from my original intent, and turned all of that into updated instructions in my repo's .github/copilot-instructions.md file. Automatically. Think about that for a second. The tool watched where it struggled, identified the patterns in my corrections, and wrote better instructions for itself. There's another variation - /chronicle tips. This one looks at how you're working and suggests where you could be prompting better, using features you haven't discovered, or breaking habits that are costing you time. /chronicle improve makes Copilot smarter about your repo. /chronicle tips makes you smarter about Copilot. Run both periodically. Curious if others have stumbled on this. What else are you finding in Copilot that most people haven't noticed yet.

Stop Fighting Your CI Environment: Meet action-zephyr-setup 🛠️🤖 If you’re building with Zephyr RTOS, you know the "CI Tax." Setting up a GitHub runner usually involves a mountain of boilerplate: installing the SDK, managing Python dependencies, and wrestling with west init and west update logic. The official https://lnkd.in/dYZ2dcmm changes the game. As a Senior Engineer, I’m always looking for ways to reduce "DevOps Friction" so the team can focus on code, not infrastructure. This GitHub Action is now my go-to for a reason. Why this belongs in your .github/workflows: 🔹 One-Stop Shop: It handles the West workspace initialization, SDK downloading, and toolchain setup in a single step. 🔹Flexible Toolchains: Need arm-zephyr-eabi? Or maybe riscv64? You can specify exactly what you need without bloating the runner. 🔹Smart Filtering: It supports west-group-filter, allowing you to skip heavy modules (like HALs you aren't using) to slash your CI build times. ⏱️ 🔹Consistency: It ensures that every developer on the team - and every CI runner - is using the exact same environment version. Gone are the days of maintaining custom Docker images just to run a simple sanity check. How are you handling Zephyr CI? Are you still using custom containers, or have you migrated to optimized GitHub Actions? #EmbeddedSystems #ZephyrRTOS #CI #DevOps #FirmwareEngineering #GitHubActions #Automation #IoT

If you are new to the GitHub Copilot CLI, this video is for you! I know the terminal can feel like a learning curve (it was for me), but adding Copilot changes everything. You no longer need to memorize complex syntax—you just ask for what you want in plain English, and it does not even need to be spelled correctly! In my newest tutorial, I strip away the complexity and focus purely on getting you up and running from scratch. Here is exactly what we cover to get you started: 🔹 What the Copilot CLI actually is (and how it differs from regular Copilot Chat) 🔹 A painless, step-by-step installation guide using PowerShell 🔹 How to seamlessly integrate it right into your VS Code terminal 🔹 The core commands you need to start navigating and executing tasks with AI 🔹 Create and use your first Agent for a .NET code migration Once we have the basics down, I even give you a sneak peek into some advanced features—like Custom Agents and YOLO mode—so you can see what's possible once you get comfortable. Ready to stop treating your terminal like a typewriter and let AI do the heavy lifting? Watch the complete beginner-friendly guide here: https://lnkd.in/gkkFumqs #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #VSCode #LogicAppsAviators