GitHub Copilot CLI: AI-Powered Terminal Companion

GitHub Copilot CLI: Turn the terminal into an AI-powered teammate ⚡️ 🚀 GitHub Copilot CLI is stepping beyond autocomplete—straight into your terminal as an AI companion. ✅ Multi-file context awareness (yes, across files + even images 🖼️) 🛠️ Generate + modify code fast 🤝 Interactive help OR headless automation for CI/CD 🔌 MCP server integration + lifecycle hooks 🧠 Built-in agents + custom agents via .agent.md 🔥 Standout modes: • Interactive (local, prompt-driven) • Headless (automation pipelines) • Server (JSON-RPC for remote clients) 💡 Power moves to try: /@ mention files to give instant context /plan before coding for cleaner execution /review for high-signal code + security checks /skills + /plugin to make repeatable workflows effortless 👀 Biggest question: which would save more time—custom agents, headless CI, or code reviews? http://localhost:5204/l/c8bf6518 #GitHubCopilot #DevTools #AIEngineering #CLI #DevOps

GitHub Copilot CLI: Turn the terminal into an AI-powered teammate ⚡️ 🚀 GitHub Copilot CLI is stepping beyond autocomplete—straight into your terminal as an AI companion. ✅ Multi-file context awareness (yes, across files + even images 🖼️) 🛠️ Generate + modify code fast 🤝 Interactive help OR headless automation for CI/CD 🔌 MCP server integration + lifecycle hooks 🧠 Built-in agents + custom agents via .agent.md 🔥 Standout modes: • Interactive (local, prompt-driven) • Headless (automation pipelines) • Server (JSON-RPC for remote clients) 💡 Power moves to try: /@ mention files to give instant context /plan before coding for cleaner execution /review for high-signal code + security checks /skills + /plugin to make repeatable workflows effortless 👀 Biggest question: which would save more time—custom agents, headless CI, or code reviews? http://localhost:5204/l/c8bf6518 #GitHubCopilot #DevTools #AIEngineering #CLI #DevOps

GitHub Copilot and Claude Code may both use slash commands, but they are not the same thing. Here is the simplest way to think about it: Built-in GitHub Copilot slash commands Controlled by GitHub and Microsoft Available based on your VS Code version, Copilot features, and extensions Designed for built-in actions inside Copilot Chat Examples: /explain, /fix, /tests, /doc, /new, /help Custom slash commands in Claude Code User-defined command patterns Used to shape how Claude responds Helpful for formatting, tone, structure, reasoning, and analysis Examples: /TLDR, /ELI5, /CHECKLIST, /SWOT, /COMPARE, /STEP-BY-STEP Claude Code skills Reusable automations for tasks you do often Great for turning repeated workflows into commands Examples: /review, /security, /optimize, /a11y, /test-plan My takeaway: GitHub Copilot slash commands = built-in product features Claude Code custom commands = flexible response controls Claude Code skills = repeatable workflow automation This distinction matters because many people see “slash commands” and assume they all work the same way. They do not. #GitHubCopilot #ClaudeCode #AI #DeveloperTools #VSCode #SoftwareDevelopment #Productivity #GenAI #Coding

Most of us use GitHub Copilot like autocomplete… I felt the same while building a full-stack system. It kept giving: generic code inefficient business logic - giving the universal logics instead of Architecture oriented zero awareness of system architecture So I tried something different 👇 👉 Instead of writing better prompts, I designed a system around Copilot. Custom agents (like roles for AI) Global instructions Domain skills + repo context Result? What has been the Outcome. Copilot stopped guessing… and started behaving like a context-aware engineer. I wrote a full breakdown + case study here: 👉 https://lnkd.in/guzTgCEY Big takeaway: AI doesn’t get better with prompts. It gets better with structure. Curious — how are you using Copilot today? Still prompting… or building systems around it? 👀 #AI #GitHubCopilot #SoftwareEngineering #DeveloperTools #BuildInPublic #MachineLearning

10 Best Practices for GitHub Copilot's /fleet Command /fleet lets you run multiple AI agents in parallel. One prompt. Multiple sub-agents. All working simultaneously. Here's how to use it like a pro: 1. Write artifact-specific prompts "Create docs/auth.md, docs/api.md, docs/deploy.md" — not "improve docs." Each sub-agent needs a clear deliverable. 2. Declare dependencies explicitly If index.md depends on other files, say so. The orchestrator respects the order. 3. Pick tasks that are truly independent Separate files, separate modules, batch refactoring. Parallel = no shared state. 4. Avoid monolithic prompts Vague prompts force sequential execution. Specificity unlocks parallelism. 5. Use custom agents for specialized work Point test generation to @test-writer, docs to @docs-agent. Right tool for each sub-task. 6. Review merge conflicts carefully More parallel changes = more potential collisions. Extra oversight is non-negotiable. 7. Use --no-ask-user for CI/CD Running /fleet in pipelines? Add the flag. No human in the loop = no blocking prompts. 8. Monitor with Agent HQ GitHub's Mission Control lets you view, compare, and triage all agent sessions from one dashboard. 9. Set up copilot-instructions.md Global coding conventions + repo-specific context = consistent output across all parallel agents. 10. Track usage and costs More agents = more credits burned. Set budgets per project. Parallel power isn't free. /fleet turns your weekend maintenance backlog into a 20-minute task. The developers who learn to orchestrate agents will outpace those who code alone. #GitHubCopilot #FleetCommand #CopilotCLI #AgenticAI #MultiAgent #DeveloperProductivity

GitHub Copilot is no longer just a chatbot it's a teammate with its own computer. 1. It Has Its Own "Brain" (The Runner) Most users think Copilot just predicts text. Fact: When an Agent handles a complex task, GitHub spins up a hidden Linux container. The Agent uses this "sandbox" to run your code, execute tests, and fix bugs in real-time before you ever see the final result. It’s not guessing; it’s experimenting. 2. Context Beyond the Code (MCP) Copilot Agents are breaking out of the IDE. Using the Model Context Protocol (MCP), they can now "see" outside your code. They can read your Jira tickets, search Slack conversations for requirements, and check Sentry logs to find why a production build failed. It’s the first time an AI tool has "peripheral vision" across your whole company. 3. The Hidden "AGENTS.md" Pro developers are moving past simple prompts and using AGENTS.md files. By placing this in your repo, you provide a "Manual" for the AI. It forces the Agent to follow your specific architectural rules—like "never use this library" or "always wrap API calls in this specific handler"—making the AI act like a senior dev who already knows your codebase. 4. Automatic Self-Correction Before an Agent submits a Pull Request, it runs a Self-Security Scan. If it accidentally writes a security vulnerability (like an SQL injection), it detects it using built-in CodeQL tools, wipes the code, and rewrites it. It essentially peer-reviews itself so you don't have to. 5. Model Agnostic Logic The Agent isn't "stuck" with one AI. It uses dynamic routing. It might use a fast model for simple edits, but if you ask for a complex refactor, it automatically switches to a high-reasoning model (like Claude 3.5 or Gemini) to handle the heavy lifting. It chooses the best "brain" for the job. #GitHub #AIAgents #SoftwareEngineering #FutureOfWork #GitHubCopilot

Stop writing manual technical documentation. Watch this instead. 👇 I recorded a quick 2-mint raw demo showing exactly how the new WhyLog platform automatically turns your GitHub PRs into a searchable engineering brain. In the video, you’ll see the full workflow in action: 1️⃣ A developer merges a Pull Request in GitHub. 2️⃣ WhyLog instantly extracts the architectural "why" and sends a human-readable summary to Slack. 3️⃣ The Tech Lead clicks "Approve" right inside Slack. 4️⃣ The team uses our "Ask Why" AI feature to search past decisions instantly, without ever opening a documentation tool. The disconnect between writing code and writing docs is officially solved. Check out the demo to see how clean the workflow is, and let me know your brutally honest feedback in the comments! You can try it on your own repos here: https://lnkd.in/d49CVX_x #BuildInPublic #DeveloperTools #SoftwareEngineering #Automation #Nextjs #WhyLog

Modernising legacy applications is a challenge many teams face, especially when balancing the need for innovation with maintaining stability. This video from the .NET team demonstrates how GitHub Copilot can assist in modernising .NET applications directly within VS Code. I appreciate how it showcases practical examples of leveraging AI to refactor and update codebases, which can be a daunting task for developers. Tools like these can help reduce the cognitive load and speed up the process, allowing teams to focus on delivering value rather than getting bogged down in repetitive tasks. If you’re exploring ways to bring older systems up to date or considering how AI can support your development workflows, this is worth a watch. It’s a great reminder of how far our tools have come and how they can empower us to tackle complex problems more effectively. #dotnet #vscode #githubcopilot

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