GitHub Copilot Code Review Now Agentic and AI-Powered

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

I opened VS Code today, typed nothing… and somehow still managed to “spend tokens.” For a second, I thought Copilot had developed trust issues. Turns out—it’s just very prepared. 😄 I went down a rabbit hole to understand what GitHub Copilot is actually doing behind the scenes, and this completely changed how I think about AI-assisted coding: Fresh sessions aren’t really “empty” Even before you type a single character, Copilot is already loading context like: System prompt + tool definitions Your workspace file structure Instruction files (.instructions.md) User memory, skills, and agent registries So yeah… your “blank editor” isn’t blank at all. Here’s the interesting part 👇 You actually have control over how much gets loaded. Tweak the applyTo frontmatter to limit which instruction files auto-load Or convert them into .prompt.md files so they only activate when you call them Result? Less unnecessary context → fewer tokens used → faster, more focused suggestions. It’s a tiny config change, but in a large enterprise codebase, this can make a serious difference in performance and cost. Sometimes productivity isn’t about adding more tools— it’s about understanding what your tools are already doing. #GitHubCopilot #AI #DeveloperProductivity #VSCode #CodingTips #SoftwareEngineering

Usually I've been using Github copilot specifically in tasks like making a Frontend for my apps, helping me shape up ideas, plan the process of putting an idea into reality, and It has been a really good help, I notice that using LLM tools for backend tasks isn't really good unless you define the architecture a priori and define proper guardrails such as Skills, needs, specs etc. and even then depending on the model i noticed a lot of drift ! for example, with Opus 4.5/4.6 It was such a wonderful experience ( minus the payment part ) but lately the quality has dropped significantly, and that makes COMPLETE sense because how is anthropic able to afford giving such access to these frontier models for such a low cost ? Now, I get surprised that Copilot Pro doesn't even have access to frontier anthropic models ! it only has access to Opus 4.7 but at a cost rate of 7.5X tokens, which no one in their right mind is going to use for personal development unless it's funded by an enterprise for specific tasks. Claude Code is a good tool but i fear that by using these tools directly I would lose touch with writing code, would not gain experience/make mistakes that can be REALLY helpful for me. I m kind of jealous of developers that existed before the AI era, because they were allowed to make mistakes, got proper code reviews from mentors, had to go through the pain of figuring out how things work by reading forums/manuals. Now it's convenient to find solutions through AI, but then I feel like you lose the power of innovation when it's necessary, you lose precious experience, and you don't grow at the same rate these old developers do. Maybe the definition of a software dev is changing ? .. maybe. But what is certain is, the interviews aren't, a senior engineer capabilities are still tested the same way as years ago, so you have to continue growing while also being productive to meet ROIs.

One tool that quietly changed my daily workflow: GitHub Copilot. Not because it writes perfect code. But because it removes friction. Things that used to take minutes… Now take seconds. Writing boilerplate. Creating DTOs. Generating test cases. Handling repetitive logic. And that adds up. The real value of Copilot isn’t just speed. It’s momentum. You stay in flow longer. You switch context less. You explore ideas faster. But here’s what makes the difference: How you use it. Copilot is powerful when: 🔹 You know what you’re building 🔹 You can review and validate suggestions 🔹 You guide it with clear intent It’s not a shortcut for thinking. It’s a tool that amplifies it. The developers who benefit the most are not beginners… They’re the ones who already understand the fundamentals. Because they know what to accept. And what to reject. In the end, Copilot doesn’t make you a better engineer. But it can make a good engineer… significantly faster. How has GitHub Copilot changed your workflow? #GitHubCopilot #AI #SoftwareEngineering #Java #Developers #Productivity #Coding #Tech

GitHub Copilot makes you a faster engineer. Devin tries to be one. That's the sharpest way to describe the difference. Copilot lives in your IDE and suggests the next line. Devin gets a task, opens a shell, writes code, runs tests, reads errors, searches docs, and opens a pull request -- without you touching a keyboard in between. Cognition Labs launched Devin in March 2024 with a demo that went viral. A team of 10 people, 10 IOI gold medals between them, building what they called the "first AI software engineer." The benchmark number that circulated: Devin resolved 13.86% of real GitHub issues on SWE-Bench unassisted. The previous best was 1.96%. That's not a marginal improvement. That's a category shift. What does this mean practically? You can hand Devin a scoped ticket -- "add pagination to this endpoint with tests" -- and come back to a PR. The feedback loop runs inside Devin's environment, not through you. It's not magic. It struggles with ambiguous requirements, novel architectures, and anything requiring product judgment. And you should absolutely review what it produces. But the workflow shift is real: from writing code to reviewing code. Day 1 of my #45DayDevinChallenge. Starting with the fundamentals before going deep on prompting, Playbooks, integrations, and the parts that actually matter in production. Refer in detail Medium post on the topic : https://lnkd.in/gJm2ddrB What's your experience with autonomous agents vs. copilot-style tools -- and which has actually changed how you work? #DevinAI #SoftwareEngineering #AIAgents

After spending months deep in large refactoring projects with both tools, here’s my honest take as a developer who loves powerful models but values control even more: Claude models are absolutely top-notch. Their reasoning depth, ability to handle complex architecture, multi-step logic, and subtle edge cases is still best-in-class in 2026. When I need serious thinking power, I reach for Claude every time. But the harness makes all the difference.🤌 GitHub Copilot’s integration in VS Code simply feels more developer-friendly to me: ✅ Inline diffs I can review chunk-by-chunk ✅ The explicit “Keep”/accept workflow that lets me stay in the driver’s seat ✅ Better visibility into exactly what’s changing without constant context-switching ✅ A tighter, more predictable loop where I decide what sticks With Claude Code (even in the improved VS Code extension), I often find myself fighting context compaction😒, less granular acceptance, and that slight “black-box” feeling on bigger sessions - despite the incredible model underneath. It’s not that Claude Code is bad - far from it. The agentic power is unmatched for certain heavy lifts. But for my daily flow, where I want to see, review, selectively accept, and maintain full control, Copilot’s harness just clicks better right now. This isn’t a “one is better” story. It’s a reminder that model intelligence ≠ developer experience. The best setup for many of us is using both: Copilot for the everyday visible, controllable coding loop + Claude when raw reasoning muscle is required. What’s your experience? 🤔 Do you prefer the tight IDE harness (Copilot style) or the powerful agentic terminal-first approach (Claude Code) where you end up spending more than you need? #AICoding #DeveloperTools #GitHubCopilot #ClaudeCode #VSCode #SoftwareEngineering

🚨 You're using GitHub Copilot wrong — and it's costing you hours every week. Most developers just open Copilot and start chatting. But without context, Copilot is just guessing about your stack, your conventions, and your project structure. The fix? Repository Custom Instructions. One file. Permanent memory. A smarter AI assistant for your entire team. Here's what you can do with it 👇 🟢 Create a .github/copilot-instructions.md file to give Copilot a permanent project brief — your stack, build commands, coding rules, and folder structure 🟢 Add path-specific instruction files in .github/instructions/ to apply different rules to different parts of your codebase (frontend vs backend vs tests) 🟢 Use an AGENTS.md file to guide the Copilot cloud agent so it can write PRs that actually pass your CI on the first try 🟢 Control scope with glob patterns — target only TypeScript files, only Python files in a specific folder, or your entire repo 🟢 Use excludeAgent in your frontmatter to restrict certain instructions to either code review or the cloud agent — not both 🟢 Create prompt files (.github/prompts/) for repeatable tasks like "generate a new API endpoint" or "write a unit test" — invoke them in one command 🟢 Custom instructions work across VS Code, Visual Studio, JetBrains, Xcode, and the GitHub web UI 🟢 All instruction types stack together — personal, repository, and organization instructions all apply, with personal taking highest priority The result? Copilot stops suggesting the wrong test framework. The cloud agent stops breaking your build. Code reviews align with your actual standards. One markdown file → a permanently smarter AI that knows your project like a teammate. I wrote a full step-by-step guide on Medium covering everything from setup to pro tips: https://lnkd.in/g-QuhhnF If this helped, drop a ♻️ to share it with your team. #GitHubCopilot #AITools #DeveloperProductivity #SoftwareEngineering #Coding #AIAssistant #GenerativeAI #DevTools #TechTips #DevCommunity #FutureOfWork #VSCode #CodeQuality #ProgrammingTips #Automation

GitHub Copilot is only as good as your Framework’s "Secret Sauce." 🍝🛠️ Are you still using AI just for boilerplate snippets? You are missing the real power. As an SDET, my challenge isn’t writing more code; it's enforcing consistent, high-quality code across the team. If Copilot suggests a generic locator while your framework demands a strict Page Object Model with custom logging, you’ve just inherited tech debt. The Pro-SDET Strategy: I use a .github/copilot-instructions.md file (or specialized .prompt files) in my project root to customize Copilot’s brain. I am training my AI Assistant to think like my Lead Architect. Here are my rules: 1. Strict POM: "Always inherit from BasePage. Never initialize locators directly in the test file." 2. Locator Hierarchy: "If a data-testid exists, use that. Never generate XPaths with indices. Prioritize user-facing roles." 3. Traceability: "Every major browser action must use test.step() for reporting." The Result: I type // create a test for user profile update, and Copilot doesn’t just write code—it writes my specific framework code. This reduces my PR review and refactoring time by over 80%. Stop fighting generic AI suggestions; start engineering your AI to enforce your team's standards. #SDETTips #AutomationArchitecture #GithubCopilot #AIinTesting #PageObjectModel #CodeQuality #TestAutomationStrategy #QualityEngineering #CleanCode

Cursor vs GitHub After 6 months of deep evaluation across multiple engineering teams, the developer experience gap is wider than expected. SETUP & ONBOARDING: Cursor wins decisively here. Download, authenticate, and you're coding with AI in under 5 minutes. GitHub requires VS Code setup, extension management, and often wrestling with authentication flows that can take 20-30 minutes for new team members. DOCUMENTATION QUALITY: GitHub Copilot benefits from Microsoft's enterprise documentation machine - comprehensive but sometimes overwhelming. Cursor's docs are leaner, more example-driven, and get developers to their "aha moment" faster. SDK & INTEGRATION: This is where it gets interesting. Copilot's tight VS Code integration means familiar keybindings and workflows. But Cursor's purpose-built environment offers features like AI-powered refactoring and codebase-wide context that feel genuinely next-generation. DEVELOPER HAPPINESS: Our internal surveys show 73% preference for Cursor among developers who've used both for 30+ days. The key differentiator? Less friction between thought and code. The surprising insight: tool switching costs are lower than we assumed. Most teams can evaluate both in a sprint. Which tool has transformed your team's velocity the most? See the full comparison: https://lnkd.in/e2fGGryV #Cursor #GitHubCopilot #DeveloperExperience

Every NuGet package looked brilliant in its README too. A year ago I was using GitHub Copilot for basic autocomplete. Today, 95–99% of code in our .NET projects is AI-generated — we negotiate architecture with agents in plan mode, implement full features including tests in one go, and use GitHub and Confluence MCP tools to ground every decision in our actual codebase. The productivity gains are real. I'm not here to argue otherwise. But here's what I now tell my team: treat AI like a NuGet package with a great README, not like a senior peer. The happy path works in ten minutes and you feel brilliant. Then staging blows up. A version conflict breaks your injection container. A leaky abstraction surfaces under load. The original maintainer went dark six months ago and now you own a security vulnerability buried in three lines of code you never actually read. That's not a .NET cautionary tale. That's an AI story playing out in production systems right now. METR's 2025 randomised controlled trial found experienced developers were 19% slower using AI tools — despite predicting they'd be 24% faster. The bottleneck has shifted. It's no longer writing code. It's absorption capacity: can your team genuinely understand, own, and debug what's being shipped? Every line of AI-generated code is not an asset. It's a maintenance contract you signed without reading the terms. The shift I'm making: code author to Editor-in-Chief. Before you hit Tab on that ghost text, pause three seconds. Don't ask "does this look right?" Ask: "if this throws a NullReferenceException at 3am on Sunday, do I know exactly why.. or would I have to ask the AI to explain my own system back to me?" If it's the latter, the model is in control. Not you. Stop asking how much code you can generate. Start asking how much you can maintain. What's your test for knowing you still own your codebase - and AI is just the tool? #DotNET #SoftwareEngineering #AIEngineering #TechLeadership #EnterpriseArchitecture #CSharp #AgenticAI