GitHub Copilot CLI adds ACP server support for easier coding agent integration

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

Most teams use GitHub Copilot like autocomplete. The best teams structure their projects so Copilot works like a real engineering assistant. GitHub Copilot gets much better when your repo gives it clear context. Not just code. But instructions, conventions, workflows, and boundaries. A practical GitHub Copilot project structure could look like this: /.github/copilot-instructions.md Core guidance for how Copilot should behave across the repository. /.github/instructions/ Modular instruction files for specific domains: 👉 frontend.instructions.md 👉 backend.instructions.md 👉 testing.instructions.md 👉 api.instructions.md /docs/architecture/ System design, domain boundaries, key technical decisions. /docs/conventions/ Naming rules, folder patterns, coding style, review expectations. /prompts/ Reusable prompts for recurring tasks: 🎯 refactoring 🎯 writing tests 🎯 generating API handlers 🎯 reviewing PRs /examples/ Golden examples Copilot can mimic: 🎯 preferred component patterns 🎯 API route templates 🎯 test structure 🎯 error handling patterns /scripts/ Helper scripts for validation, formatting, codegen, and local workflows. The point is simple: AI coding tools perform better in well-structured environments. If your repository is messy, undocumented, and inconsistent, Copilot will reflect that. If your repository is structured, explicit, and opinionated, Copilot becomes far more useful for: 👉 generating code that matches your standards 👉 following existing architecture 👉 producing better tests 👉 reducing review friction 👉 speeding up onboarding Good AI output starts with good project design. We should stop asking only: "Which AI coding tool is best?" And start asking: "Is our codebase designed to make AI effective?" That is where the real leverage is. #GitHubCopilot #AIEngineering #DeveloperTools #SoftwareArchitecture #EngineeringProductivity #CodingWithAI #DevTools #ContextEngineering

🚀 GitHub Copilot is no longer just autocomplete — it’s evolving into a full AI development ecosystem. What started as an AI pair programmer is now expanding across IDE, GitHub, Terminal, and CI/CD, helping developers plan, generate, test, review, and ship code faster than ever. In this article, I explore: 🔹 Copilot Workspace Commands and AI-native development 🔹 How Copilot operates across every surface where developers work 🔹 Why the future of coding is shifting from writing code to directing software creation 📖 Part 1 of my GitHub Copilot series: https://lnkd.in/d32hYykQ More deep dives coming next on: ➡️ Copilot in IDEs ➡️ Copilot in GitHub workflows ➡️ Copilot in the terminal and CI/CD pipelines If you're building software in 2026, understanding this shift isn’t optional — it’s a competitive advantage. #GitHubCopilot #AIinDev #DeveloperProductivity #AICoding #SoftwareArchitecture #DevTools #AITransformation

🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷 🔷 🔷🔷🔷 GitHub #Copilot #CLI’s new /fleet #command 🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷🔷 it enables developers to run #multiple #specialized #agents in #parallel, coordinated by an #orchestrator. This approach #decomposes #complex, #multi-file tasks into #smaller, #independent work items, #executes them #concurrently, and then synthesizes results. #Developers can define clear #deliverables, #dependencies, and #constraints in their #prompts, while monitoring #progress with /tasks. #Custom #agents can be configured for #specialized roles, and non‑interactive runs are supported. 🎯 #core #key #points----> 🔑#Faster #execution : tasks are completed more quickly through parallel agent work. 🔑#Structured #task #management : clear decomposition and orchestration of complex objectives. 🔑#Scalable #collaboration: multiple agents can contribute simultaneously to larger projects. 🔑#Caution #required : developers must manage file boundaries carefully to avoid overwrite conflicts. 🎯#Quick #starter #checklist (actionable)🎯 👉#Pick a task with clear parallelism (multi-file refactor, docs for multiple components). 👉#Draft a deliverable‑oriented /fleet prompt with file paths, constraints, and validation steps. 👉#Optionally add custom agents in .github/agents/ for specialized roles. 👉#Run /fleet <prompt> interactively or copilot -p "/fleet <prompt>" --no-ask-user non‑interactively. 👉#Monitor with /tasks, review decomposition, and verify lint/tests pass before marking done. https://lnkd.in/dsrnabKE

🚀 10 Tips to Effectively Leverage GitHub Copilot in Terminal As developers move from AI-assisted coding to AI-orchestrated engineering, GitHub Copilot in the terminal is quietly becoming one of the most powerful productivity layers in the SDLC. Here are 10 practical commands that can unlock agentic workflows directly from your terminal: ✅ /fleet Run multiple custom agents in parallel to accelerate complex workflows ✅ /chronicles tips Analyze Copilot usage patterns and get data-driven suggestions to improve developer productivity ✅ /chronicles improve Identify and resolve friction points across your application or workflow ✅ /research Investigate potential vulnerabilities and security issues proactively ✅ /delegate Ship review fixes automatically as a Pull Request ✅ /review Review code using custom organizational instructions or guardrails ✅ /compact Summarize conversation history to optimize context usage ✅ /plan Break down complex tasks into structured, multi-phase execution plans ✅ /agent Browse and select from available custom agents for specific engineering tasks ✅ /skills Manage and enhance agent capabilities for specialized outcomes 💡 We're increasingly seeing enterprises move from: Code Generation → Task Automation → Multi-Agent Execution Terminal‑native AI workflows are becoming the new control plane for AI‑native engineering. #GitHubCopilot #AgenticAI #DevEx #AIinSDLC #PlatformEngineering #DeveloperProductivity

Code at the Speed of Thought with GitHub Copilot CLI ⚡️💡 Diving into the new GitHub Copilot CLI write-up and feeling inspired — bringing agentic AI straight into the terminal is a game changer for how we iterate and ship code 🚀💻. The CLI-first approach keeps context in your repo, speeds up routine tasks, and even lets you delegate well-defined work to agents so you can focus on higher‑value problems. Tried a few quick prompts in my head and the possibilities stood out: faster prototyping, context-aware suggestions, and less context switching between editor, browser, and terminal. For teams, that means smoother reviews, quicker PRs, and more time for design and architecture thinking. ⚙️✨ If you’re a developer or engineering lead, it’s worth exploring how a CLI workflow could fit into your stack — small changes to tooling can unlock big productivity wins. https://lnkd.in/dU8uyJzq #GitHub #Copilot #CLI #AI #Productivity #DevTools #DeveloperExperience

GitHub Copilot just changed the rules of the game. CLI activity now counts toward usage metrics-finally, our terminal work isn’t invisible anymore. This feels like a long-overdue acknowledgment that modern developers don’t live in IDEs alone. It’s not just about code completion anymore; it’s about how we actually work. Teams will now see a clearer picture of where AI is adding value-whether in the terminal or the editor. But does this mean we’re being more accurately billed, or simply tracked more thoroughly? What does this shift mean for your team’s productivity and budgeting? #GitHubCopilot #DeveloperTools #AIinDevelopment #TechLeadership #Productivity

GitHub Copilot is no longer an autocomplete feature. I want to make sure every developer understands this shift: GitHub Copilot is now an Agentic platform, and VS Code has become an extension of that platform. This isn't marketing speak—it's a fundamental change in how we interact with AI-powered development tools. What does "agentic" actually mean here? → Copilot can now work autonomously in the background → It can open pull requests, fix bugs, and add tests independently → It operates like a peer programmer, not just a pair programmer → VS Code shifted to weekly releases (starting with v1.111) specifically to keep pace with this rapid evolution The implications for engineering teams are significant: 1. Developers can delegate routine tasks while focusing on architecture and complex problem-solving 2. Technical debt cleanup and test coverage improvements can happen asynchronously 3. The line between "writing code" and "directing AI agents" is blurring fast Microsoft's decision to move VS Code from monthly to weekly releases tells you everything about the pace of change in this space. They're betting that faster iteration beats stability predictability in the current AI development landscape. The question isn't whether agentic AI will transform software development—it's whether your team is positioned to leverage it effectively. What's your experience with Copilot's new capabilities? Are you using the coding agent features yet? #AgenticAI #GitHubCopilot #SoftwareDevelopment

I just published a new GitHub Community article: GitHub Copilot for Diagrams, Humans for Architectural Decisions. For many teams, diagrams are essential—but creating and maintaining them is still slow, manual, and inconsistent. In this post, I share a practical view of how GitHub Copilot can help accelerate diagram work with Ask, Plan, and Agent mode—while keeping architectural decisions where they belong: with humans. Would love to hear how others are using Copilot for documentation and diagram workflows. #GitHub #GitHubCopilot #SoftwareArchitecture #AI #Mermaid #Documentation https://lnkd.in/eqjfk-BM

I've been using GitHub Copilot CLI daily, and these 5 slash commands have fundamentally changed how I work in the terminal. Here's what every developer should know: /mcp → Manage Model Context Protocol integrations. Connect to external tools and data sources without leaving your workflow. /skills → List and add skills to your solution. Extend Copilot's capabilities with custom functionality. /agent → Browse available agents or create new ones tailored to your needs. /diff → Review file changes with the ability to leave inline comments. Just press 'C' to comment on any change. /terminal setup → A one-time command that enables shift+enter support for better terminal interaction. But here's what surprised me most: The /pr create and /pr fix commands are absolute game-changers. I used to just prompt "create a PR" but /pr create handles everything — ensuring branches are up to date, proper formatting, the works. And /pr fix? It checks all CI failures in your PR, fixes issues, and even handles merge conflicts automatically. With 90% of Fortune 100 companies now deploying Copilot and developers reporting 55% faster task completion, these aren't just nice-to-haves — they're essential productivity multipliers. Full command reference: https://lnkd.in/gc9vMsE6 What Copilot CLI commands have transformed your workflow? #GitHubCopilot #DeveloperProductivity #AIinDevelopment