GitHub Copilot Skills Simplify Automation

We're moving to agentic development at SignNow, and chose Sortie AI to orchestrate the agents. When the work shifts from "developer writes code" to "agent writes code, developer reviews," orchestration becomes the bottleneck. Something has to poll Jira, pick up a ticket, spin up an agent, open a PR, and hand it off down the pipeline. Doing this manually just moves the toil from writing code to launching agents. We evaluated five options: Sortie AI, OpenAI Symphony, Composio AO, GitHub Copilot Cloud Agent, and Cursor 3.0, and chose Sortie. Here's why: Data sovereignty. Our code never leaves our infrastructure. Jira-native polling. No webhooks, no glue. Sortie watches Jira labels and picks up issues automatically. Claude Code and Copilot as first-class runtimes. Both supported natively, no adapter shims. Persistence across restarts. Sortie uses embedded SQLite in WAL mode. Retry counts, run history, and handoff state survive crashes and deploys. This is what ruled out Composio AO, which has no persistence layer. Config-as-code. Every workflow is a WORKFLOW.md file with YAML frontmatter and a Go-templated prompt. Lives in Git, reviewed in PRs, deployed via GitOps.  Multi-step pipelines. "specify -> plan -> implement" as three chained workflows sharing a branch per issue statuses. Simpler services run a single-step pipeline. Simpler services get a single-step pipeline. Single binary, zero dependencies. ~17 MB Go binary. No Kubernetes, no queues, no PostgreSQL. Out of the box: CI feedback loops, review comment routing, budget controls, Prometheus metrics, and agent introspection via MCP tools.  Please share which orchestrator you trust with your codebase? #AgentsOrchestration #CodingAgents #ClaudeCode #Copilot

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 Folder + Copilot in VS Code — Hidden Power Most Developers Miss Most developers use it just to push code… But ignore the real control layer of the repository 👇 📁 ".github" folder When combined with GitHub Copilot in , it becomes a powerful system for automation, collaboration, and AI-driven development. 🔹 What is ".github"? A special folder that controls: ✔ Automation (CI/CD) ✔ Project rules & standards ✔ Team collaboration ✔ AI behavior (via skill.md) 💡 Think: Brain of your repository 🔹 Key Components You Should Know 📄 "skill.md" 👉 Guides Copilot to follow your coding standards 👉 Makes AI context-aware ⚙️ "workflows/" (GitHub Actions) 👉 Automate build, test, deploy 👉 CI/CD pipelines 📄 "pull_request_template.md" 👉 Standard PR format 👉 Better code reviews 📁 "ISSUE_TEMPLATE/" 👉 Structured bug reports & feature requests 👥 "CODEOWNERS" 👉 Auto-assign reviewers 👉 Clear ownership 🔄 "dependabot.yml" 👉 Automatic dependency updates 👉 Security improvements 🔐 "SECURITY.md" 👉 Vulnerability reporting process 🤝 "CONTRIBUTING.md" & "CODE_OF_CONDUCT.md" 👉 Better onboarding & collaboration 🔹 How it enhances Copilot in VS Code 💻 With GitHub Copilot: ✔ Reads repository context ✔ Uses ".github" rules as guidance ✔ "skill.md" shapes AI responses 👉 Result: project-specific, consistent, production-ready suggestions 🔹 Real Impact ✅ Smarter AI suggestions ✅ Consistent codebase ✅ Faster onboarding ✅ Reduced review effort ✅ Stronger automation 🔹 Final Thought «🧠 ".github" = Brain 🤖 Copilot = Assistant» 👉 Together, they transform how modern development works #GitHub #GitHubCopilot #VSCode #AI #SoftwareDevelopment #DevOps #CleanCode #DeveloperProductivity

🚀 Introducing KubeAid v0.0.1 A faster way to debug Kubernetes incidents Kubernetes debugging can be slow, noisy, and frustrating—especially during real incidents. So I built KubeAid ⚡ A CLI designed to reduce time-to-root-cause for developers, SREs, and platform teams. 💡 What KubeAid does Instead of jumping across multiple tools, KubeAid gives you: 🔍 Unified workload analysis (status + events + logs + resource signals) 📊 Health scoring (0–100) Quickly understand how bad things really are 🤖 AI-assisted remediation Smart suggestions + deterministic fallback 📄 Exportable reports (Text / JSON / HTML) for smooth incident handoffs 🛠️ What’s inside v0.0.1 ✅ Detects CrashLoopBackOff + inspects previous container logs ✅ Provides actionable diagnosis with next-step commands ✅ Supports context-aware CLI workflows ✅ Generates structured reports for teams & automation 🎥 Demo I recorded a full end-to-end demo showing: 👉 A failing Kubernetes app 👉 Complete debugging workflow 👉 How KubeAid surfaces root cause quickly 🔗 Links 📦 Repo: https://lnkd.in/gSBvTYw8 🚀 Release: https://lnkd.in/dhNmRGiw 🎬 Demo: https://lnkd.in/gfGTJKcj 💬 Looking for feedback If you're working with Kubernetes in production, I’d love your thoughts 🙌 What would make this tool truly useful for you? #Kubernetes #DevOps #SRE #CloudNative #PlatformEngineering #OpenSource #CLI #Observability #IncidentResponse #GitHub

🚀 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

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 Launches New AI-Generated Software Framework for Developers 📌 GitHub Copilot unleashes a new AI-generated software framework, transforming dev workflows from snippets to full ecosystems - think encrypted vaults and remote shells. Vibe coding is no longer fantasy; it’s powering 41% of 2025 code, with giants like Snap using AI for over 65%. DevOps teams now wield agentic tools, GPU-accelerated SDKs, and context-rich models to rebuild systems faster - and smarter. 🔗 Read more: https://lnkd.in/djMtQtKC #Githubcopilot #Llm #Vibecoding #Softwareframework #Developertool

Built a centralized command center with the #GithubCopilotSDK to manage all my projects from one place. Now I can jump into any project, invoke the right agent when I need it, and keep everything flowing without constantly bouncing between tools, folders, and contexts. Less tab chaos. Less mental reload. More actual work getting done, because apparently developers are now expected to be herding AIs. The real power shows up when multiple agents can run across multiple projects in parallel. I can instruct an agent to work on one project, switch to another project with one click, and send another agent there without breaking flow. The command center can also clone a project so multiple agents can work on the same codebase without stepping on each other. That opens up a much cleaner way to parallelise work, test ideas faster, and keep progress moving without collisions. The biggest win has been reducing context-switching cost. Instead of reloading my brain every time I move between projects, I have one control layer that helps me stay focused, move faster, and keep momentum. This is the kind of workflow I think more developers will move toward: AI not as a gimmick sitting in one editor tab, but as an operational layer across your entire working environment. Productivity is not just about writing code faster. It is about staying in the zone longer. #GitHubCopilot #CopilotSDK #AIEngineering #DeveloperProductivity #Automation #AgenticAI #SoftwareEngineering

🚀 Anthropic just introduced a feature that turns Claude Code into a background worker. Until now, Claude Code needed your terminal running to do anything. Routines run on Anthropic's cloud, so that's no longer true. How it works: you define a routine in the CLI or in a desktop app, point it at a repo, and give it access to whatever it needs (Slack, Jira, Linear, GitHub). Claude handles searching, writing, and testing on its own in a sandboxed environment. Three ways to trigger a routine: 🔹 GitHub webhooks. Fires when a PR opens, a release ships, or a merge lands. 🔹 Cron schedule. Daily, weekly, or whatever cadence you want. 🔹 Direct API call. Wire it into your CD pipeline or alerting tools. Three use cases you should pay attention to: 🔹 Morning PR summaries. A Slack report of overnight changes and anything that looks architecturally risky. 🔹 Backlog triage. Claude reads new issues, checks the codebase, and suggests fixes or applies labels. 🔹 Post-deploy verification. Smoke tests and log checks triggered automatically after every merge. Curious how you’d use this in your workflow — what would you automate first? Check out the full documentation: https://lnkd.in/dZDtWhNH #AI #ClaudeCode #Anthropic #SoftwareEngineering #Automation #DevTools #Productivity

I built a 𝗹𝗶𝘃𝗶𝗻𝗴 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗴𝗿𝗮𝗽𝗵 of my entire DevOps operation — and my AI pair programmer maintains it for me. 114 interconnected notes. Guides, reference docs, task logs, monitoring runbooks, infrastructure checklists — all linked in a single Obsidian vault with a Mermaid-powered dependency graph. Here's what makes it different: 🤖 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱, 𝗵𝘂𝗺𝗮𝗻-𝗰𝘂𝗿𝗮𝘁𝗲𝗱. Every note is created through conversation with GitHub Copilot in VS Code. I describe the problem, we solve it together, and the solution becomes a permanent, searchable artifact. 🕸️ 𝗜𝘁'𝘀 𝗮 𝗴𝗿𝗮𝗽𝗵, 𝗻𝗼𝘁 𝗮 𝗳𝗼𝗹𝗱𝗲𝗿. Notes don't just sit in directories — they reference each other. A Grafana dashboard fix links to the telemetry map, which links to the monitoring stack, which connects to the pipeline that deploys it. Context is never lost. 📸 𝗖𝗼𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗯𝗮𝗰𝗸𝘂𝗽 𝗶𝗻 𝟯𝟬 𝗺𝗶𝗻𝘂𝘁𝗲𝘀. When I learned I might lose access to our Confluence, I asked Copilot to harvest 24 critical pages, convert them to Markdown, add frontmatter, cross-link them, and update the knowledge graph. Done before my coffee got cold. 🔄 𝗜𝘁 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝘀. Every task I complete adds to the graph. Sprint analyses, PR reviews, troubleshooting sessions — they all become reusable knowledge. Six months in, I rarely Google the same thing twice. The graph in the image shows the actual vault: color-coded clusters for active tasks (pink), completed work (purple), reference material (blue), guides (green), and monitoring (yellow). Every arrow is a real contextual link. This isn't about replacing documentation — it's about making documentation that 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗴𝗲𝘁𝘀 𝘂𝘀𝗲𝗱. Tools: Obsidian + GitHub Copilot (Claude) + Mermaid.js + a simple note-naming convention. What's your system for capturing operational knowledge? #DevOps #KnowledgeManagement #AI #GitHubCopilot #Obsidian #InfrastructureAsCode #Documentation #DeveloperProductivity