Unlock GitHub Copilot's Power with .github/copilot-instructions.md

🚀 Stop working sequentially. Start working in parallel. GitHub Copilot CLI just introduced "/fleet" — a powerful new command that lets you dispatch multiple AI sub-agents simultaneously across your codebase. Instead of tackling tasks one by one, Copilot now acts like an orchestrator: - Breaks down your objective - Identifies independent workstreams - Executes them in parallel This is a major shift—from using AI as a single assistant to leveraging it as an entire engineering team. 💡 Perfect for: • Refactoring across multiple files at once • Generating documentation for multiple components simultaneously • Building end-to-end features spanning API, UI, and tests in one pass If you're already using GitHub Copilot, "/fleet" is one of those features that instantly changes how you work. The future of software development isn't just AI-assisted—it's AI-orchestrated. #GitHubCopilot #AI #DevTools #SoftwareEngineering #Productivity #DeveloperTools #Coding #ArtificialIntelligence

Been using Copilot heavily at work recently, and noticed something interesting… Small observation while using GitHub Copilot inside VS Code / IntelliJ. The biggest risk isn’t wrong code. It’s how easily we accept code without fully processing it. You’re in flow → Copilot suggests → you hit Tab And keep moving. But later: → The logic doesn’t fully match your system → It introduces a silent assumption → Or it breaks consistency with existing code Also noticed this in larger files: Suggestions start drifting away from earlier patterns Even within the same class. The issue isn’t intelligence. It’s a subtle UX gap: Speed is optimized. Awareness isn’t. Feels like a small fix could make a big difference: → Show a quick diff or highlight what’s being introduced → Surface assumptions in the suggestion → Add a lightweight “what this does” preview before acceptance Because in AI-assisted coding: Speed helps you write faster But awareness helps you write better And over time, that’s what really matters. #AI #GitHubCopilot #DeveloperExperience #ProductThinking #SoftwareEngineering

You are not using GitHub Copilot wrong. But you might be using it too shallowly. I saw a team generating code line-by-line with Copilot. Every function… Every selector… Every assertion… That’s not productivity. That’s assisted typing. We changed one thing: 👉 Asked Copilot for patterns, not lines • Page objects instead of selectors • Reusable flows instead of steps • Utilities instead of repetition Result? • 3x faster delivery • 60% less rework Copilot is not a keyboard upgrade. It’s a design accelerator. #AI #Copilot #Automation #QA

Anatomy of a Custom Agent Skill for your GitHub Copilot Agent 🚀 Stop treating your AI agent like a generalist! 🛑 Give it the specific expert knowledge it needs to master your project’s unique architecture. 🧠✨ #CustomSkills allow you to extend GitHub Copilot’s capabilities using nothing more than simple Markdown files. 📝 No complex backends, no heavy lifting, just clear instructions and high-quality context. ⚡️ Why this is a game-changer: 🎯 Precision: Guide the LLM to use specific libraries and internal patterns. ⚡ Efficiency: Trigger the right "tool" automatically via YAML metadata. 🛠️ Low Code: If you can write a README, you can build a Copilot Skill! I’ve open-sourced the full breakdown and integration guides in my #promptingblueprints repository. 📂⭐ Explore the tutorials here: 🔹 The Anatomy: https://lnkd.in/dsAcwMcU 🔍 🔹 The Integration: https://lnkd.in/dHNb3EsT ⚙️ Within our #DAiTA Platform at Österreichische Post AG Business Solutions, we utilize specialized skills to streamline AI-driven development and provide a robust skills layer for Agentic Frameworks. 📄🚀 📖 Read more about this approach in our blog: https://lnkd.in/dEc2ijrd Check out the image to see how simple the SKILL.md structure really is! #GitHubCopilot #AISDLC #VSCode #PromptEngineering #SoftwareDevelopment #DeveloperExperience #Coding

We just completed two GitHub Copilot sessions! Our teams had the chance to attend GitHub Copilot for Developers and Copilot for Managers. Both sessions were eye‑opening: developers discovered how Copilot can speed up coding, and managers gained clarity on how AI can support productivity, governance, and delivery. Super insightful, practical, and immediately actionable. Excited to see how AI will elevate the way we work! Seeing both technical and non‑technical roles align around the same goal: leveraging AI to build smarter, faster, and more efficiently. AI isn’t replacing developers, it’s empowering them. AI isn’t confusing managers, it’s giving them visibility and confidence. A big thanks to the facilitator for the guidance and energy (Mickaël LOPES, Rosan Vingalalon) and their experts (Thierry Madkaud , Julien Strebler & Damien Aicheh)

Been using GitHub Copilot (agent mode) for automation - feature files, step defs, page objects… all of it. And wow… this thing has confidence. 😂 Give it a vague BRD? It will happily invent logic like it’s part of the requirements. Generic prompt? You’ll get generic nonsense back. Lesson learned real quick: You don’t “use” Copilot - you manage it. That said… Debugging is ridiculously fast now. And yes, it sometimes generates code so complex I just stare at it… but hey, it works 🤷♀️ Big takeaway: Copilot is basically that overconfident teammate who moves fast, breaks things, but somehow still gets the job done - if you guide it well. Anyone else seeing this? Or is it just me fighting my AI coworker daily? #AIinQA #GitHubCopilot #SoftwareTesting #AutomationTesting #AIinTech #ShiftLeft #FutureOfWork

Have you heard about Copilot Skills? What about Copilot Instructions? The difference is key to save tokens and you typing the same thing over and over. 🔹 Instructions = the always-on rulebook that run any time you write a prompt. e.g. Project conventions that apply everywhere. Naming, frameworks, guardrails. Lives in context on every task. 𝘊𝘢𝘳𝘦𝘧𝘶𝘭 𝘤𝘢𝘶𝘴𝘦 𝘪𝘵 𝘤𝘢𝘯 𝘣𝘶𝘳𝘯 𝘵𝘰𝘬𝘦𝘯𝘴 𝘳𝘶𝘯𝘯𝘪𝘯𝘨 𝘦𝘷𝘦𝘳𝘺 𝘵𝘪𝘮𝘦 🔸 Skills = On-demand playbooks. Run as needed, buuut can occasionally be missed. e.g. workflows that only load when a specific task triggers them or multi-step procedures without bloating your always-on context. You need the above to have AI do the mundane tasks AND so you don't burn tokens and have to retype the same prompts. 👇Want a full tutorial? See the link in the comments on how to be a Super Architect, join me and other attendees as we attend the Pro Code conference #UDPP26 remotely! The rule of thumb: → "This is how we write code here" → Instruction → "When asked to do X, follow this procedure" → Skill Example from a Dataverse project:  • Instruction: "Publisher prefix is contoso_. Tables use singular PascalCase." • Skill: create-dataverse-column — enforces description-is-never-blank, prompts for type constraints, asks whether to add to forms and views. The Claude vs Github icon setup in the diagram is just cause I thought it looked cool. They both have these concepts. Fair warning - make sure you check with Github copilot how it decides which one to use, I noticed it leans Instruction when it meant to make a skill. #GitHubCopilot #ClaudeCode #AIEngineering #DeveloperTools #Dataverse

Attended “Copilot Day” at my university Faisalabad Institute of Science and Technology today — focused on how GitHub Copilot is shaping the way developers work. One session that stood out was on building as an “Agentic Developer” using Copilot agents — less manual coding, more guiding and orchestrating. Key takeaways: - Writing code is no longer the bottleneck — thinking clearly is - Developers who leverage AI effectively will move significantly faster - The shift is from “coding everything” to “reviewing, guiding, and shipping” - Tools like Copilot are changing daily workflows, not just speeding them up Also got to interact with other developers and see how people are already integrating AI into real projects. This isn’t future talk anymore — it’s already happening. #GitHub #Copilot #AI #SoftwareDevelopment #Developers