Standardizing AI coding with shared context, project constraints, examples, types, versions, and skills

Plugins, skills, and MCPs let you customize Cursor for your workflows. The new Customize page brings them into one place.

You can now add and manage plugins, skills, MCPs, subagents, rules, commands, and hooks at the user, team, or workspace level, and even bring your own custom MCPs.

Cursor now shows you a leaderboard of the most popular plugins, skills, and MCPs across your team.

Add any to your setup with one click from the new Customize page and extend Cursor for your workflow.

Plugins now support prebuilt canvases: shared setup templates your team can open and reuse.

Use the Hex Canvas to build data visualizations.

Use the Atlassian Canvas to see a realtime view of all our issues, projects, and documents.

Team marketplaces now support imports of plugin repositories from GitLab, BitBucket, or Azure DevOps so you easily add plugins and distribute them to your team.

Most "Claude made me 10x faster" posts are about generation — scaffold this, write that function. The usage data I keep coming back to points the other way.

A senior engineer at Sentry actually instrumented her own Claude usage across 116 sessions and classified every one. The split: 67% comprehension, 2% code generation. She works in a codebase that's 15+ years old with ~100 PRs merged a day — and in that environment the bottleneck was never "write the function." It was "why does this exist," "where did this regression come from," "what changed while I was out."

Here's the part worth stealing...

Copilot code review now supports repository-level `AGENTS.md` files, and it’s easier to request a review from Copilot on draft pull requests with the Request button. These changes are all generally available.

You can now add an `AGENTS.md` file at the root of your repository to help shape Copilot code review feedback. If your repository already has an `AGENTS.md` file, Copilot code review will now utilize that context automatically as part of its workflow. This makes it easier to get reviews that better reflect your repository’s conventions and expectations.

You can already request Copilot code review on draft pull requests to get a first pass on your draft before even publishing your pull request. Now, the Request button shows up next to Copilot on draft pull requests. This makes that workflow easier by letting you request Copilot directly from the reviewer picker without needing to search for Copilot first.

On the Conversation tab of your pull request, Copilot code review events documented in the timeline can get noisy. We’ve collapsed certain Copilot code review timeline events together to help declutter your conversation tab, allowing you to find what matters, quickly.

Now in early beta for SuperGrok Heavy subscribers — Grok Build is a new coding agent that runs right from your terminal.

Today we're launching an early beta of Grok Build, a powerful new coding agent and CLI for professional software engineering and complex coding work.

Okay so the Flutter and Dart teams just shipped something quietly cool — official Agent Skills repositories, and they work with basically every major AI coding assistant out there.

Here's the quick rundown. There are two official repos now:

`flutter/skills` — layouts, routing, JSON serialization, integration tests, overflow fixes

`dart-lang/skills` — unit test gen, pub dependency resolution, static analysis fixes Install them into your project with one command:

npx skills add flutter/skills --skill '*' --agent universal
npx skills add dart-lang/skills --skill '*' --agent universal

That dumps everything into `.agents/skills` and your agent (Claude Code, Cursor, Copilot, Antigravity, whatever you're using) just... picks them up automatically.

So why is this actually interesting and not just more AI slop??

Instead of cramming a wall of instructions into a rules file and hoping the agent remembers them, skills use progressive disclosure — the agent reads just the metadata first, then pulls in the full instructions only when it needs them for a specific task. Context window stays lean, and the agent gets laser-focused guidance exactly when it matters.

Think of it like giving your AI a proper runbook instead of just vibes.

And it goes further — the `skills` CLI on pub.dev can pull skills directly from your dependency tree, meaning packages could eventually ship their own skills alongside their code. Imagine adding a package and your AI agent automatically knows how to use it correctly.

Are you already using agent skills or custom rules files in your Flutter projects? Has AI-assisted Flutter dev actually clicked for you yet, or does it still feel like more trouble than it's worth? And which AI assistant are you reaching for most — Claude Code, Cursor, something else?

AI Product Management Software Development

One-shotting is everywhere right now. Write a single prompt, maybe drop in a screenshot, and watch a full app materialize while you keep your fingers crossed. It’s a great demo. It’s a terrible workflow.

The problem isn’t that AI can’t generate a lot. It can. The problem is that a massive all-in-one output gives you no seams to work with. If something is wrong, which is less likely with frontier models but never impossible, you’re either redoing the whole thing or accepting the mess. There’s no middle ground.

One-shotting also doesn’t scale with complexity. A simple landing page, sure. A real product with multiple pages, components, navigation, visual consistency? The output starts degrading fast. You end up with something that looks right in a screenshot and breaks in production.

This gets worse in a team. If everyone is one-shotting independently, there’s no shared reference point. Components drift. Decisions contradict each other. A spec gives the team a common language. It also gives the AI a common language across multiple sessions. It’s the difference between everyone improvising and everyone playing from the same sheet.

I’ve been through this, and I think I’ve figured it out.

Last week a colleague and I were presenting the AI coding setup we’d built. The full show: fine-grained instruction files, custom agents with scoped responsibilities, reusable skills, and to top it all off, an orchestrator agent tying it all together. It looked impressive.

Then someone in the audience asked: “That’s cool, but do we always need this? If we skip all that and keep it simple, what do we actually lose?”

Over the past year, I changed my perspective on AI in software development. I used to be skeptical, but after watching Burke Holland's content on Copilot agents, I realized that I had to give it another try. From using AI as a simple code completion tool, to a tool to generate tests or simple isolated enhancements and smaller tasks, I started to see it as a system that's capable of doing much more. Fast forward to today, I'm using it more and more to create new features from scratch.

Over the last month I used Agentic AI to rewrite a project entirely from scratch, and the results are amazing. The speed boost is incredible, and the quality of the code is good, not perfect, but good enough in my opinion. In this blog post, I want to share my experience and thoughts on AI in software development, and why I think it's a game-changer for software development.

How I actually use AI in software development today, why context matters more than hype, and why this gets much harder in SAP.

Every developer has a workflow they've spent years refining.

Mine lives almost entirely in the terminal. I'm SSH'd into a development machine. I organize my sessions with tmux, edit code in Vim, and use git worktrees to juggle multiple tasks in parallel.

When people talk about AI-native development, the conversation sometimes assumes developers will converge on the same new workflow. A new IDE, a new interface, a new way of building software.