Skills vs AGENTS.md in claude codex and cursor

In this video, I compare Skills and AGENTS.md workflows in Claude Codex and Cursor, focusing on how each approach changes the way I structure context, delegate tasks, and keep AI-assisted coding predictable as projects grow.

I walk through the practical differences between embedding instructions in an AGENTS.md file versus using Skills as reusable, task-focused capabilities. The core idea is not just which one is “better,” but how each model affects maintainability, discoverability, and execution quality when I’m working across real codebases instead of toy examples. If you use AI coding tools daily, the distinction matters a lot once prompts become repetitive, multi-step, or dependent on project conventions.

A big part of the discussion is how AGENTS.md acts like a persistent instruction layer for an agent operating inside a repository. It can be helpful when I want the assistant to inherit project rules, coding standards, architecture expectations, testing habits, or deployment constraints. That makes it useful for setting a stable operating environment. But I also look at the tradeoffs: instruction sprawl, ambiguity, uneven reuse between repositories, and the tendency for large instruction files to become generic policy documents instead of sharp execution tools.

On the other side, I cover how Skills can be a cleaner fit when I want modular, intentional, reusable workflows. Instead of relying on one broad instruction file to shape everything, Skills let me define focused capabilities for specific tasks like debugging, refactoring, writing migrations, generating tests, or reviewing architecture boundaries. That can make AI behavior easier to invoke on demand and easier to improve over time because the behavior is packaged around a concrete job rather than buried inside a general-purpose repo document.

I also dig into how this plays out differently in Claude Codex and Cursor. Even when two tools support similar ideas, the ergonomics of context handling, task invocation, and instruction layering can lead to very different outcomes. The video highlights where these systems feel explicit versus implicit, how much control I have over the agent’s working style, and what happens when I need consistency across multiple sessions or contributors.

A specific technical use case I discuss is maintaining a TypeScript monorepo with a Next.js frontend, a Node.js API, shared packages, and a PostgreSQL database managed through Prisma. In that setup, AGENTS.md can define repo-wide rules such as import boundaries, naming conventions, test requirements, migration safety checks, and deployment expectations. Skills then become useful for targeted workflows like generating a Prisma migration safely, updating API contracts, writing regression tests for a failing endpoint, or refactoring a shared package without breaking downstream apps. In practice, this split can reduce prompt repetition and help the assistant stay aligned with both architectural rules and task-specific procedures.

I also explore when AGENTS.md becomes the right default, when Skills provide a stronger abstraction, and when combining both produces the best result. For example, a repository may benefit from a lightweight AGENTS.md that defines the operating constraints of the codebase, while Skills handle repeatable higher-value actions that need structure, checklists, and stronger execution patterns. That balance can be especially useful if I want the assistant to behave consistently without turning every interaction into a long custom prompt.

If you’re trying to improve AI-assisted development in Claude Codex or Cursor, this video is about choosing the right layer for instructions, reuse, and control. I focus on the real workflow implications: how to reduce friction, avoid duplicated prompting, make agent behavior more reliable, and design a setup that scales from quick edits to more complex engineering tasks.

This is especially relevant if you’re building with large repositories, strict conventions, multiple contributors, or repeated implementation patterns. The better your instruction model is, the easier it becomes to turn AI from a chat helper into a dependable part of your development process.

If you’ve been deciding between repo-wide agent instructions and reusable task modules, this video breaks down the practical differences and why the choice can materially affect code quality, speed, and confidence.

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Видео Skills vs AGENTS.md in claude codex and cursor канала Mike Møller Nielsen