Claude Code and GitHub Copilot Prompting Best Practices

GitHub Copilot Pulls Drawstring On Tighter Developer Usage Limits GitHub Copilot is popular. The AI-powered code completion tool (originally developed by GitHub and OpenAI) works to give software application developers a so-called “AI pair programmer” buddy that offers suggested code snippets and (when called upon) entire functions – and it happens directly within an engineer’s Integrated Development Environment (IDE) of choice. All of which means that GitHub Copilot isn’t just popular in terms of total usage; the tool is reporting an increase in patterns of high concurrency (individual developers performing similar operations, but more likely different developers requesting the same types of functions) and intense usage among power-users....

🚀 6 Free GitHub Repos That Can Save You $100+/Month on Claude Code If you're deep in Claude for daily coding and your token bill is creeping up, this one's for you. I came across this brilliant hand-drawn breakdown (image above) and had to share it. These 6 open-source tools are quietly helping developers cut costs while getting sharper, faster, and more useful outputs. Here’s the quick hit list: 1. vercel-labs/agent-browser Replaces Claude in Chrome. Uses the accessibility tree instead of screenshots or messy HTML. Clean context, zero noise. 2. rtk-ai/rtk 60-90% faster on common commands (npm run, git commit, pnpm test, etc.). Real-world savings of 20-30% in actual workflows. 3. juliusbrussee/caveman The “terse-output” skill we all needed. Drops the fluff and filler. Responses become direct: “use map(), not filter(). Return acc.” 4. tirth8205/code-review-graph Up to 49× fewer tokens on daily coding tasks via AST mapping. One of the biggest efficiency wins on the list. 5. Gronsten/claude-usage-monitor Real-time 5-hour usage window + live active-session token tracking. Know exactly where you stand before you hit the cap. 6. phuryn/claude-usage Historical breakdowns by session, day, and week. Beautiful charts showing exactly where your tokens (and money) went — and what to fix. The real magic? Stack them. Use what you need. Save money. Ship more. Compounding wins. These are all free, actively maintained, and built by the community. If you’re serious about getting more signal and less spend from Claude, go star them now. Which one are you trying first? Or drop your own favorite Claude optimization tool in the comments 👇 Let’s help each other build faster and cheaper. #ClaudeAI #Anthropic #AIforDevelopers #GitHub #DeveloperTools #TokenOptimization #Productivity #OpenSource #SaveMoney

I reported a bug to GitHub. They fixed it in 2 days—then revamped their entire extension system. Here's what happened: While using GitHub Copilot CLI's extension system, I discovered a critical issue: creating a hook in an extension would override all global hooks. This broke my hook flows—the system I use to harden security across all my repositories. So I filed an issue. Within one week: • Root cause identified • Fix shipped to production • Complete extension system overhaul released The new capabilities are significant: → Custom slash commands now supported in the SDK → UI elicitation dialogs for structured user input → In-session management via /extensions command → Multi-language SDK support (Node.js, Python, Go, .NET) → Hot reload without full session restart This isn't just a bug fix. It's a signal. GitHub is treating Copilot CLI extensions as a first-class extensibility platform. For teams building internal tooling, security enforcement, or custom workflows—this changes the game. The speed of iteration here is remarkable. From power-user secret to documented, multi-language platform in 9 days. We're entering an era where developer feedback directly shapes the AI tools we use daily. If you're not experimenting with Copilot CLI extensions yet, now is the time. Full story in the video. Link in comments. #GitHubCopilot #DeveloperExperience #DevTools

A developer just starred your GitHub repo. That's not a vanity metric. That's a developer saying "this might solve my problem." The next thing they do is look for a video. A quickstart. A demo. A two-minute walkthrough that shows them whether this tool is worth an hour of their time. If that video is technically wrong, unclear, or made by someone who doesn't understand what they just starred - the conversion is dead. The star might stay. The user doesn't. Most developer tools companies obsess over the star count. Nobody tracks what happens in the 10 minutes after. What's the first video a developer finds after discovering your tool, and when did you last check if it's actually good?

GitHub Copilot is no longer a nice-to-have for developers. It is becoming partof the way modern organisations build. I have seen too much developer time disappear into repetitive tasks, contextswitching and rewriting patterns that are already familiar. That is exactly why I wrote this piece. GitHub Copilot is not just about faster code. It helps reduce mental load sodevelopers can focus on solving real business problems instead of repeatingthe same steps over and over again. If your organisation is building in Business Central and your teams are stilldoing everything the hard way, now is the right time to rethink that approach. Read the blog here: https://bit.ly/4seJfkS Reach out to me: 📩 channel@4sight.cloud #4SightInsights #GitHubCopilot #BusinessCentral #ALDevelopment#DeveloperProductivity #AIForBusiness

I've been experimenting with GitHub Copilot in a way that I think most developers haven't tried yet — and I wanted to share the workflow I've landed on. Start in the Repository. When you're building something new, go straight to your GitHub repository and describe what you want Copilot to build. From a completely empty repo. No existing code, no setup, nothing. Just tell it what you want and let it go to work. I did exactly that — brand new repository, described my app, and walked away. It scaffolded the entire thing and opened a PR for my review. Then use Issues to refine. Once you have a foundation in place, that's when GitHub Issues becomes powerful. Open an issue, type @copilot, and describe what you want changed, added, or fixed. It reads your existing code and makes targeted changes in context. So the mental model I'd suggest: New project? Start in the repository and let Copilot build the foundation Existing project? Use Issues to extend, refine, or correct Think of it like handing an architect an empty lot versus asking a contractor to renovate what's already there. Two different jobs, two different starting points. Most people are still using Copilot just for autocomplete. The real power is in delegating entire workstreams. #GitHub #GitHubCopilot #AIEngineering #DeveloperProductivity #SoftwareDevelopment #CodingSmarter #AI #DevTools #Microsoft

You assign a GitHub issue before lunch. By the time you're back — there's a pull request waiting. That's the GitHub Copilot Coding Agent. GitHub Copilot has evolved far beyond autocomplete. The Coding Agent now works asynchronously in the background — fixing bugs, writing tests, refactoring code — and hands you a ready-to-review PR when it's done. Here's what just shipped: 🎛️ Model picker — Choose Claude Opus, Claude Sonnet, GPT-Codex-Max, or let Auto decide. Pick the right model for the complexity of each task. 🔍 Self-review — The agent reviews its own diff before tagging you. By the time you're looking at it, someone already went through it once. 🔒 Built-in security — Code scanning, secret scanning & dependency vulnerability checks — all before the PR opens. Free with Copilot coding agent. 🔌 MCP servers — Plug in external tools, databases, and context via Model Context Protocol. Your agent now has eyes beyond the repo. The agent boots a VM, clones your repo, RAG-indexes your codebase, and starts coding. You track every step in session logs. Your branch protections, CI/CD approvals, and security posture? Untouched. Think of it as having a junior dev who never sleeps, never skips tests, and always opens a clean PR. What low-to-medium complexity tasks would you hand off to an agent first? Drop a comment 👇 #GitHubCopilot #AI #CodingAgent #SoftwareEngineering #DevTools #AgenticAI #GitHub

Hey folks 👋 after my last post i.e (building a design system on Claude) a consistent concern was the friction of maintaining a Claude skill (design system) across different workspaces updating it, re-uploading it, sending it over Slack again and again. It just wasn’t sustainable for a real team setup. So… to solve this problem👇 We moved everything to GitHub cloud-based platform 😼 Now upload the entire design system repo foundations, tokens, rules and that becomes the single source of truth. I connect that repo directly to Claude Code using GitHub CLI, and that’s it. Now when anyone on the team pulls it into Claude Code, they’re all working from the same live version. Update the repo → everyone gets the update. ☕ No manual sharing. No version confusion. No drift. If you want to try it, here’s the quick setup to connect github with claude code 1️⃣ Open Terminal Press Cmd + Space → type Terminal → hit Enter 2️⃣ Install Homebrew Paste this command and press Enter: /bin/bash -c "$(curl -fsSL https://lnkd.in/dedBGCGW)" It will ask for your Mac password → type it and press Enter (you won’t see the characters while typing that’s normal) Wait ~5 minutes for it to install Type "done" when Homebrew setup finish 3️⃣ Install GitHub CLI brew install gh 4️⃣ Log into GitHub gh auth login Choose: GitHub.com → HTTPS → Yes → Login with a web browser Then: You’ll get a one-time code (e.g. ABCD-1234) Copy it, press Enter → your browser will open Paste the code → click Authorize → done ✅ Once it’s connected, you can call the repo directly inside Claude Code. The design system stops being “a file someone owns” and becomes a shared, versioned, cloud-based system the whole team designers, devs, PMs can rely on. That’s really the collaboration piece that was missing. 🔁 Still early days, but this is the closest we’ve come to something that actually works at scale. P.S. There is a bit of a learning curve (especially if Terminal isn’t your thing), but once it’s set up, you’re basically done. #claude #ai #github #claudecode #terminal #productdesigners #designsystem

GitHub Copilot is a pair programmer that suggests code snippets and full functions in real time inside your editor. It reads the surrounding code and comments to autocomplete patterns, draft unit tests, scaffold endpoints, and handle repetitive glue work. Best for developers who want to move faster and cut boilerplate without breaking flow. Use it to spike features, explore unfamiliar APIs, and standardize routine code. Guide it with clear function names and comments, review suggestions like any pull request, and keep security checks in place for critical paths. #GitHubCopilot #PairProgramming #DevTools