GitHub Copilot CLI: Custom Agents and Delegation

💡 Make GitHub Copilot Work With Your Team Using Prompt Files When working with GitHub Copilot in VS Code, you can create reusable prompt files that live right alongside your source code. 📦 These files can be stored in source control and executed anytime, making them perfect for tasks your team performs repeatedly. For example, my team uses a prompt file to: ✅ Add new properties across multiple layers of our API ✅ Generate consistent release notes for those properties Since creating it, we’ve used the same prompt file to add 20 new properties, saving time and keeping our output consistent across services. ⚙️ You can even define which AI mode and model Copilot uses when running the prompt, giving you predictable, repeatable results every time. If your team uses Copilot regularly, try building a few prompt files for your common workflows. It’s a small step that makes AI collaboration more structured, consistent, and team-friendly. 💬 Have you tried using prompt files with Copilot yet? I’d love to hear how your team’s using them. #GitHubCopilot #VSCode #AIinDevelopment #DeveloperProductivity #SoftwareEngineering #CodingTools

🚀 Unlock Developer Productivity with GitHub Copilot I’ve known about GitHub Copilot for a while, but today I took a deep dive into how it really works—and wow, it’s a game-changer! It’s more than just code suggestions. It’s a true AI-powered pair programmer that can: ✅ Generate precise code modifications via chat ✅ Approve changes one by one—or all at once ✅ Create Swagger documentation for APIs ✅ Create UML flow diagrams for code functions and classes ✅ Analyze entire codebases and recommend improvements ✅ BE DEV can generate React code for front-end development ✅ Fill complex forms faster and efficiently and reduce lead time time to PROD ✅ Leverage 100s of extensions for customization Copilot doesn’t just speed up coding—it transforms how developers design, review, and deliver software. 👉 Check out this YouTube playlist https://lnkd.in/gc4efpJ2 for demos and tips. Have you tried Copilot yet? How is AI shaping your workflow? Share your thoughts below! 👇

Did you know you can give GitHub Copilot custom review instructions? I’ve started using this to automate the kind of feedback I keep repeating in pull requests. With the rise of AI-assisted code, even good contributions tend to reuse patterns or miss small team conventions. Instead of commenting the same things like “use async,” “prefer f-strings,” or “move this into a shared utils module,” I can ask Copilot to look for them automatically. All it takes is a .github/copilot-instructions.md file. You can describe what you want in plain English, such as “flag any code that calls time.sleep in async functions” or “remind authors to add type hints.” Copilot’s reviewer then incorporates those rules into its automated review. It is not perfect and sometimes misses context, but it helps enforce consistency across a codebase. Unlike a linter or pre-commit hook, it can also catch more subjective issues that are part of a team’s culture. If your team uses Copilot reviews, this feature is worth trying. It is a small change that saves a surprising amount of repetitive feedback.

🚀 New Product Update: GitHub Copilot CLI – Custom Agents & Delegation 📄 https://lnkd.in/gqw5bzaR ⸻ 🤖 What it’s about: • The update introduces custom agents within GitHub Copilot CLI. You can define agent personas that reflect your team’s workflows, code style, tools and prompts. • Custom agent configurations can live in .github/agents at repo/org level or in ~/.copilot/agents locally. • You can invoke an agent with /agent slash-command. The CLI also exposes your custom agents as tools that the Copilot model may pick in an “agentic loop” when needed. • You can delegate tasks from the CLI to the Copilot Coding Agent using /delegate TASK-DESCRIPTION. The CLI commits unstaged changes, creates a new branch, the coding agent proceeds asynchronously, opens a draft PR, and notifies you with link in terminal. • Performance improvements: streaming token-by-token output, parallel tool-calls support, reduced memory footprint, screen flickering fixes. ⸻ 💡 Why it matters: • Offers tailored automation: teams can build agents that encode their standards and tools, increasing consistency and speed. • Enables hands-off workflows: you delegate tasks and let the Copilot Coding Agent take them from branch creation to draft PR, freeing up developer time. • Improves responsiveness and scalability: streaming output and parallel tool execution reduce waiting time and bottlenecks. • Integrates deeper into your dev ecosystem: local config for agents means less friction, more control. • Makes the whole CLI tool from passive assistance to proactive autonomous agent platform. ⸻ 🧠 Takeaway: If you are working with GitHub and Copilot in a development team, this update gives you two major levers: customising agent behaviour to match your workflows, and delegating non-interactive tasks to autonomous agents. Update your CLI (npm install -g @github/copilot@latest) and explore what agents you can build.

As someone who’s always exploring AI tools to make life a bit easier, I recently decided to dive deeper into GitHub Copilot. Since I use VS Code every day, I came across this great playlist from the VS Code YouTube channel. It goes beyond just the basics — covering things like prompting strategies, Agent Mode, and even what not to do when using Copilot. Here are a few videos I found really helpful 👇 🎯 Get Started with GitHub Copilot in VS Code (2025) Good starting point — covers the basics, different use cases, available features, and extra learning resources. ▶️ https://lnkd.in/dkBEsF5W 💡 Essential AI Prompts for Developers Explains 4 prompting strategies (Role Prompt, Stepwise Chain of Thought, Pros & Cons, and Q&A Strategy) that help get better results from Copilot. ▶️ https://lnkd.in/dsw9rrNV 🤖 VS Code Agent Mode Just Changed Everything This one shows how you can use Agent Mode, MCP servers, and PRD documents to build a complete app — including the database part. ▶️ https://lnkd.in/db_kPGDU 🚫 Copilot Best Practices (What Not To Do) A nice take on best practices — explained through common mistakes people make with Copilot. ▶️ https://lnkd.in/dJ73mt9j 🎥 Full playlist (GitHub Copilot + VS Code): https://lnkd.in/dfaBTEyB) 💡 Copilot has already become an integral part of modern software development. If used right, it’s not just a productivity tool — it’s like having a pair programmer who helps you write, learn, and grow.  👇 I’d love to hear what tools or tricks you use to make your dev workflow smarter. #githubcopilot #vscode #ai #coding #copilot #developer #odoo

Exciting update from GitHub Copilot: you can now configure agent-specific instructions via .instructions.md files, including a new excludeAgent property to control when certain instructions apply (for code-review vs coding agents). This gives teams more granular control over how Copilot behaves across workflows. #GitHubCopilot #DevTools #SoftwareDelivery #developerproductivity #developers #AI #GitHub #developercommunity https://lnkd.in/gA2j3eir

🚀 Creative Use of GitHub Copilot for Code Review A few months ago, I faced an interesting challenge during a code review. One of my colleagues had pushed 35-40 files - a lot of files, limited time, and everything bundled together in a single Bitbucket branch. Even though we had access to GitHub Copilot, we couldn’t use its built-in code review features directly since the repo was hosted on Bitbucket, not GitHub. So, I decided to get creative. Here’s what I did 👇 1. I pulled the latest code locally and took all the commits that were pushed to Bitbucket, keeping them in a staged state. 2. Then, I removed the files from staging, moving them under the Changes section in my local setup. 3. Once all the modified files appeared there, I used GitHub Copilot locally to review each file one by one, by prompting it with clear instructions for what to check and analyze. 4. I then went through each of Copilot’s suggestions, verified the logic, and made adjustments where needed. This approach turned out to be surprisingly effective - Copilot helped me quickly identify potential logic issues (like an extra for loop in a setup function), while I could focus on validating the reasoning and context. 💡 Key takeaway: Even when your workflow doesn’t perfectly align with your tools, creativity and the right prompts can help you get the most out of AI. #AI #CodeReview #GitHubCopilot #Bitbucket #SoftwareEngineering #DeveloperExperience #Innovation #Productivity #MERNSTACK #REACT

Started with GitHub Copilot: 1. Set up GitHub Copilot: Install the Extension: If you are using an IDE like VS Code, navigate to the Extensions Marketplace, search for "GitHub Copilot," and click "Install." This will typically install both GitHub Copilot and GitHub Copilot Chat. 2. Basic Code Completion: Create a File: Open or create a new file in your IDE. Start Typing: Begin typing code. GitHub Copilot will provide inline suggestions in "ghost text" as you type. Accept Suggestions: Press Tab to accept the suggested code. 3. Using GitHub Copilot Chat (Agent Mode for Autonomous Coding): Open Chat: Open the Chat view (often by pressing Ctrl+Alt+I or selecting the chat icon in the IDE's title bar). Select Agent Mode: In the chat mode dropdown, select "Agent" to enable autonomous coding. Provide a Prompt: Describe the task you want Copilot to perform (e.g., "Create a basic Node.js web app for sharing recipes"). The AI will analyze your request and generate the necessary files and code. Review and Accept: Review the generated code and select "Keep" to accept the changes. 4. Using GitHub Copilot Chat (Inline Chat for Specific Tasks): Select Code: Highlight the code you want to modify or get help with in your editor. Open Inline Chat: Press Ctrl+I to open the inline chat. Ask a Question/Provide a Prompt: Ask Copilot to explain the code, refactor it, add documentation, or make other modifications directly within your file. 5. Customization and Best Practices: Context is Key: Provide clear and concise prompts, and ensure relevant files are open in your IDE to give Copilot sufficient context. Experiment with Models: In Copilot Chat, you can often switch between different language models (e.g., GPT-4, Claude) to see which performs best for your specific task. Refine Prompts: If Copilot's initial suggestions are not what you need, refine your prompts to guide it towards better outputs. Sign In and Authorize: Hover over the Copilot icon in the Status Bar (or click on it) and select "Set up Copilot" or "Login to GitHub." You will be prompted to sign in with your GitHub account and authorize the Copilot plugin. If you don't have a Copilot subscription, you may be signed up for a free trial. 

Certain models are just much better at following instructions and producing value in text representation if code. Some bleeding edge modern transformers that work on AST-level (like last year's AST-T5 research and SAGE-HLS), right now we can use AST-level chunking with RAG approach to get great code indexing at correct boundaries (unlike text-based chunking). That AST indexing combined with powerful currently broadly available LLMs such as Sonnet 4.5 is one of fastest and cleanest ways to feed LLMs relevant data. Many tools use similar indexing approach, though Claude Code notably doesn't. #Software #AI #Operations

This guide shows how GitHub Copilot now supports multi-step workflows across the editor, terminal, and GitHub. It explains how you set it up, use Agent Mode, generate tests and pull requests, review code and automate tasks to streamline your development https://lnkd.in/dvvV28Mn