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

GitHub dropping Opus from Copilot Pro is not really a model story. It is a packaging story. Agentic coding is expensive. Flat-rate pricing was always going to break. Users can accept limits. They do not accept surprises. That is why this hit so hard. My take: this is the clearest sign yet that AI dev tools will be priced around capacity, not just model quality. The real question now: cheap monthly price, or reliable agent capacity?

🚀 Most developers are using GitHub Copilot wrong. They use it like autocomplete… But ignore its most powerful feature: 👉 .github/copilot-instructions.md This file turns Copilot into a project-aware AI teammate. Once added, it: • Follows your coding standards • Respects your architecture • Uses your preferred patterns • Writes consistent code & tests 💡 Result: • Less prompting • Better code quality • Faster onboarding • Easier reviews Yet, very few teams use it. Stop treating Copilot like a tool. Start using it like a programmable engineer. #AI #GitHubCopilot #SoftwareEngineering #DeveloperProductivity #CleanCode

A few months ago, I started using GitHub Copilot, and it’s made writing code feel smoother and faster. It speeds up boilerplate code and offers smarter suggestions for functions. I usually pair it with VSCode and Playwright to test and ship features more quickly. One thing I’ve learned: don’t accept every suggestion without a careful check. Balancing AI help with manual reviews keeps my code reliable and clean. If you want to speed up your workflow but stay in control, try Copilot alongside tools like Cypress or Claude Code. How are you bringing AI into your coding routine? 🚀 #GitHubCopilot #AI #CodingLife #SoftwareEngineering #AIDrivenDevelopment

Which GenAI dev framework should be in the inner loop? BMAD. GSD. Hyper Velocity Engineering. Spec-kit. Squad… Feeling framework fatigue? 😵💫 Here’s a resource worth bookmarking: an evidence-based, structured comparison of today’s most talked-about GenAI-assisted development techniques—mapped to real scenarios. What happens after clicking ✅ 🔍 See frameworks side-by-side (what they optimize for + tradeoffs) 👤 Pick based on context: solo dev → team → enterprise-ready 🧰 Align approach with tools: GitHub Copilot, Claude Code, Cursor, Codex ⚙️ Reduce “shiny object” churn and make a decision that compounds over time https://lnkd.in/djq9PPnX #genai #softwaredevelopment #github #copilot #claude

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

Swarmia now supports GitLab as a code hosting platform 🦊 You can connect your GitLab data with Jira, Linear, and AI coding tools like Copilot, Cursor, and Claude Code — and get visibility into all of your engineering work in one place. The beta includes code metrics like cycle time and review time, issue metrics, investment balance, software capitalization, sprints, initiatives, and more. DORA metrics, work log, and PR inbox are coming soon. Read more in the changelog: https://swarmia.co/4dTsqrY

Most code documentation tools stop at “viewing.” You generate docs → skim a few pages → and move on. We noticed this happening with Everdone CodeDoc too. So we changed something fundamental. Now you can download your entire code documentation as a ZIP: - One folder with AI-ready Markdown docs (for Cursor, Copilot, Claude, etc.) - One folder with clean, human-readable docs (for clients, teams, stakeholders) Why this matters: Documentation becomes useful only when it’s used. Now you can: → Add /docs to your repo and give AI real context → Share structured documentation with clients in minutes → Build internal knowledge bases instantly Instead of docs being a one-time output, they become a working asset inside your development workflow. Try it here (first 200 files free): https://lnkd.in/dgkS7268

Is GitHub Copilot turning us into code-generating monkeys? It's the debate raging through dev circles: this AI pair programmer, powerful as it is, might be actively eroding our core skills. Here's my take: - Copilot excels at boilerplate, repetitive tasks, and even suggesting complex algorithms. This is undeniable. - But are we understanding the code it generates, or just accepting it? True debugging and problem-solving skills atrophy if we rely on it blindly. - The risk is becoming an "autocomplete jockey" rather than a deeply analytical engineer. We might lose the intuition that comes from wrestling with problems ourselves. - It's a tool, not a replacement for fundamental knowledge. Like a calculator, it's great for speed, but you still need to know math. The danger isn't Copilot itself, but how we choose to use it. It should augment our abilities, not become a crutch that prevents growth. Let's be mindful. Save this if you're wrestling with this question. Follow for more unfiltered tech takes. #AIinTech #DeveloperLife #SoftwareEngineering #FutureOfCode

I spent weeks studying how top devs use Claude Code. Most are running it at 20% of its potential. 😬 Here's the full playbook to 10X your output — same hours, zero fluff: 👇 ⚡ Write a CLAUDE.md — your AI onboarding doc 🗺️ Plan before you prompt (saves 20 min per feature) ⚙️ Build custom /commands for PRs, linting & tests 🔌 Connect MCPs — live docs, Notion, GitHub sync 📸 Paste screenshots directly into your prompts 🔀 Run parallel Claude sessions with git worktrees 🔍 Ask Claude to verify its own output (game changer) Power users report 20–40% faster shipping just from tuning their setup. The tool is the same for everyone. The approach is what separates 10X devs from the rest. Save this carousel. Share it with a dev who needs it. What's your #1 Claude Code tip? Drop it below 👇 #ClaudeCode #AIProductivity #DeveloperTools #Anthropic #10XDeveloper #SoftwareEngineering