GitHub Copilot's /fleet Command Best Practices

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

GitHub Spec Kit v0.7.3 is a practical release focused on reliability and ecosystem growth. The standout change replaces shell-based context updates with a marker-based upsert approach, helping reduce fragile automation issues across environments. It also adds new community catalog entries, docs updates, and integration visibility for Claude Code and GitHub Copilot CLI workflows. #GitHub #AI #DeveloperTools #OpenSource

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/16313d98 #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 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

Still seeing teams maintain runbooks in wikis that nobody follows step by step. The commands are documented, but every execution involves copy-pasting, tweaking parameters, and hoping you did not skip anything. GitHub Copilot Custom Skills help here. You create a SKILL.md file in .github/skills/ in your repo with YAML frontmatter describing what it does, bundle your automation scripts next to it, and Copilot in agent mode can use those instructions and scripts when you kick off the workflow from a natural language prompt. The skill file lives in source control. Reviewed in PRs, versioned alongside the code it operates on. That part is compelling on its own. Skills also fit into a broader customization stack. Instructions for coding standards, prompt files for single-shot tasks, skills for multi-step workflows with bundled scripts. Each layer has a clear role. I like that separation. Got a five-command deployment check you run after every release? Wrapping it in a SKILL.md can be a pretty quick exercise. #GitHubCopilot #DevOps #PlatformEngineering #Azure

One of my favorite AI Automation personally! Documentation rarely fails loudly — it just quietly loses users. Learn how our Drasi team used GitHub Copilot as a “synthetic new user” to continuously test their documentation and surface bugs before real developers hit them. - Documentation breaks due to silent drift, not just bad instructions - Manual doc testing does not scale for fast‑moving open source projects - Treating documentation testing as a monitoring problem is a mindset shift - GitHub Copilot can follow instructions exactly as written, exposing gaps humans miss - “Getting started” guides deserve the same rigour as production code For many developers, documentation is the product. If the first experience fails, trust is gone — and users simply move on. If you’re shipping faster than you can manually test your docs, this is well worth reading. Read the full article here: How Drasi used GitHub Copilot to find documentation bugs https://msft.it/6042QfMT4 #OpenSource #GitHubCopilot #DeveloperExperience #Documentation #AIinDev #CloudNative #CNCF #MicrosoftAdvocate #MicrosoftEmployee #DevRel

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

Your engineering backlog shouldn't be your bottleneck. What if you could turn every GitHub Issue into a verified Pull Request—while you’re offline? Meet Ghost Developer —my latest project that brings autonomous, zero-touch engineering to any repository. This isn't just another "AI autocomplete." This is a specialized multi-agent swarm built on the official Claude Code CLI that actually lives in your codebase. Here is what the Ghost does differently: Autonomous Execution: It doesn't just suggest code; it navigates your repo, runs bash commands, and performs its own tests. Event-Driven: Integrated with GitHub Webhooks. When an issue is raised, the swarm wakes up and starts building. Self-Correcting: If a test fails, the Ghost doesn't give up. It reads the error, refactors the logic, and tries again until the build passes. PR-Ready: Once the task is finished and verified, it pushes a finalized Pull Request directly to your team. I built this to solve the "Developer To-Do List" crisis. By delegating the repetitive, logic-heavy lifting to an autonomous agent, I can focus on high-level architecture while the Ghost handles the execution. It’s fast, it’s secure, and it’s open-source. The era of "AI-Assisted" is over. We’ve entered the era of the Autonomous Engineer. Check it out here: https://lnkd.in/gc2vVanN #AI #SoftwareEngineering #AgenticAI #GitHub #Claude3 #Automation #OpenSource