Does your team use different AI agents? Here’s how to standardize your products.

Lince Mathew -

When multiple developers work on the same codebase, they rarely use identical AI agents. One dev might be writing code using Cursor, another might run terminal commands through Claude Code, and yet another might rely on GitHub Copilot within VS Code.

This mix of environments introduces a new operational problem: every AI model brings its own default assumptions about how to write software.

Because these models rely on general web data rather than your specific engineering guidelines, they frequently invent conflicting terminal commands, introduce forbidden dependencies, or choose architectural patterns that violate your design principles.

To maintain code quality and structural integrity across your team, you must shift your approach. Enforcing team standards requires moving away from fragile, user-level prompt engineering and adopting deterministic, repository-level AI configurations.

By checking explicit context rules and machine-readable capabilities directly into your version control system, you treat AI behavior as code that can be tested, versioned, and audited.

The Structure of Repository-Level Configuration

AI coding platforms like Claude Code, Cursor, and GitHub Copilot have standardized how they read project context.

Instead of relying on hidden user settings, they look for specific Markdown configuration files checked directly into the root of your repository.

When an engineer triggers an AI agent, the IDE builds the model's instructions using a strict priority stack:

[Highest Priority]  User/Personal Level (Local IDE settings)
       │
       ▼
[Active Priority]   Repository Level (AGENTS.md, CLAUDE.md)
       │
       ▼
[Lowest Priority]   Organization Level (Global company compliance)

What Belongs in a Repository Config and what to Omit

To keep the agent's attention sharp, your configuration file must focus on non-obvious engineering rules, project boundaries, and exact terminal scripts.

Do Include Canonical Commands

State the exact single commands required to install dependencies, run development servers, execute specific test runners, and trigger linters. This stops the model from guessing or inventing fake CLI flags.

Do Include Architectural Constraints

Explicitly outline data flow rules, directory isolation, and forbidden dependencies.

Do NOT Include Basic Formatting

Do not waste space telling an AI to use 2-space indentation, trailing commas, or to remove unused imports. Your linter and formatter (ESLint, Prettier, Biome) handle this instantly. If an agent writes bad formatting, your local tooling should fix it automatically upon saving or committing.

Here is an example

Here is a production-ready AGENTS.md file designed to constrain an AI agent working within a TypeScript backend repository:

# AGENTS.md — Project Standards & Context

## Tech Stack & Architecture
- Core: Node.js 22, TypeScript 5.x, Fastify.
- Data Layer: Prisma ORM.
- Rule: Never query the database directly from a route handler. All database operations must be isolated inside service classes located in `src/services/`.
- Validation: Every route entry point must use TypeBox schemas for request/response validation.

## Canonical Commands
- **Install Dependencies**: `pnpm install`
- **Run Dev Server**: `pnpm dev`
- **Execute Test Suite**: `pnpm test` (Uses Vitest)
- **Run Linter & Type Check**: `pnpm lint && pnpm typecheck`

## Guardrails & Boundaries
- **Critical**: Do not modify existing files inside `src/db/migrations/`. Always generate a new migration file via `pnpm prisma migrate dev`.
- **Dependency Restrictions**: Do not install or use `moment.js` for date manipulation. Use `date-fns` to protect bundle sizes.
- **Third-Party APIs**: All external HTTP requests must go through the centralized Axios client in `src/utils/http.ts`. Do not use global `fetch`.

Checking this file into Git establishes an immutable baseline. Whether a developer opens the project in Cursor or runs a command via Claude Code, the underlying agent reads the exact same rules, eliminating conflicting code styles across your team.

Maintain Design Principles

Similar to AGENTS.md, DESIGN.md stops AI agents from generating ugly, inconsistent interfaces. When you ask an agent to build a frontend component, it usually guesses layout rules and outputs generic, mismatched styling. Dropping a DESIGN.md file into your repository root provides a machine-readable design system reference.

It translates styling constraints—like colors, tokens, and font scales—into clean Markdown and YAML that LLMs parse with high accuracy.

Production Design Configuration

Here is an example of a production-ready DESIGN.md file using the YAML token layout popularized by the VoltAgent/awesome-design-md community:`

---
version: alpha
name: PostHog-design-analysis
description: |
    A playful developer-tools system rendered on a warm cream canvas with hand-drawn hedgehog mascots dotted across every page like marginalia in a sketchbook. The chrome reads like a friendly engineering blog: olive-gray ink (#4d4f46) for body, deep olive-charcoal (#23251d) for headlines, IBM Plex Sans Variable typography in tight 1.43-line-height paragraphs, and a single saturated yellow-orange CTA pill (#f7a501) carrying every primary action. The system actively rejects the genre's typical somber dark-tech aesthetic in favor of a creamy, textbook-illustration sensibility — bordered cards stack on the cream

colors:
  primary: "#f7a501"
  primary-pressed: "#dd9001"
  primary-active: "#b17816"
  on-primary: "#23251d"
  ink: "#23251d"
  body: "#4d4f46"
  charcoal: "#33342d"
  mute: "#6c6e63"

typography:
  display-xl:
    fontFamily: IBM Plex Sans Variable
    fontSize: 36px
    fontWeight: 700
    lineHeight: 1.5
    letterSpacing: 0
  display-lg:
    fontFamily: IBM Plex Sans Variable
    fontSize: 24px
    fontWeight: 800
    lineHeight: 1.33
    letterSpacing: -0.6px
  heading-lg:
    fontFamily: IBM Plex Sans Variable
    fontSize: 21px
    fontWeight: 700
    lineHeight: 1.4
    letterSpacing: -0.5px

Deep Dive into Agent Skills

If your repository configuration file sets the boundaries for what an AI agent can do, Agent Skills provide the explicit, step-by-step capabilities for executing repetitive, highly complex tasks.

As projects grow, stuffing every operational guideline, database schema pattern, and deployment manual directly into a single AGENTS.md file creates a clear technical bottleneck: it overwhelms the model's active memory and consumes your token budget on every single chat interaction.

To prevent this context bloat, modern AI workflows rely on a pattern called Progressive Disclosure. Instead of loading your entire documentation stack upfront, the repository hosts an index of modular capability folders inside a dedicated directory, such as .claude/skills/ or .github/skills/.

The AI agent scans the metadata of these skills at startup and only evaluates the complete execution instructions when a developer runs a matching trigger phrase.

The Component Hierarchy of an Agent Skill

A functional agent skill is structured as a self-contained folder containing three key files:

your-project-root/
└── .github/skills/
    └── scaffold-feature/
        ├── SKILL.md        <-- Activation triggers and step-by-step SOP
        ├── templates/
        │   └── service.ts  <-- Standard boilerplate code structure
        └── scripts/
            └── verify.sh   <-- Local validation runner
  • The Metadata & Trigger Block: Frontmatter text that tells the agent exactly when to initialize the skill based on specific user intent.
  • The Standard Operating Procedure (SOP): Precise, linear instructions explaining how the agent must manipulate files, run local tools, or format outputs.
  • The Local Deterministic Assets: Accompanying shell scripts or file templates bundled inside the skill folder that the agent must execute or copy.

Here is the example:

Here is an example of an automated feature-scaffolding skill (SKILL.md) that enforces structural uniformity when an agent is told to create a new backend resource:

---
name: scaffold-feature
description: Automatically builds a new API resource following the repository-level service layer pattern.
triggers:
  - "create a new api resource"
  - "scaffold feature"
  - "generate endpoint"
---

## Prerequisites
1. Before writing any files, ask the user for the singular, camelCase name of the resource (e.g., `userProfile`).
2. Verify that the file `src/services/${resourceName}.service.ts` does not already exist.

## Step-by-Step Procedure

### 1. Generate the Service Layer
- Read the structural template from `templates/service.ts`.
- Replace the placeholder tokens with the user's resource name.
- Write the final file to `src/services/${resourceName}.service.ts`.

### 2. Register the Route Handler
- Open `src/routes/index.ts`.
- Locate the main routing array and import the newly generated service class.
- Inject the resource route using the pattern: `server.register(${resourceName}Routes, { prefix: '/v1/${resourceName}' })`.

### 3. Validate and Verify
- Run the local verification script inside the skill directory: `./scripts/verify.sh`.
- If the script returns a non-zero exit code, read the compiler output, fix the syntax error, and re-run the script. Do not ask the user for assistance unless verification fails three times.

Instead of writing every single agent skill from scratch, you can browse SkillsMP to find and audit over 1.2 million community-made skills. It indexes public GitHub repositories so you can easily search, review, and drop pre-built SKILL.md files right into your repository for tools like Claude Code or Codex.

Generating the Skeleton

Honestly, writing strict rules and modular skill files from scratch is a bit of a chore. Instead of staring at a blank screen and guessing how to format everything for Claude Code or Cursor, you can let visual builders do the heavy lifting.

For example, design.dev has a visual AGENTS.md Generator that does exactly this. It asks you a few simple questions about your tech stack, test runners, and build commands, then spits out a perfectly structured, error-free markdown file that your AI agents can read and follow instantly.

Visual Design Systems with the DESIGN.md Generator

AI agents often struggle to maintain visual consistency when building user interfaces. This is where DESIGN.md comes in. It acts as a visual editor for Google's open design system specification.

You can visually edit color tokens, typography scales, and spacing directly in the browser and watch a live UI preview render in real-time. It even runs on-the-fly WCAG AA contrast linting to prevent accessibility issues before your code is ever generated. Once you are satisfied, the generator outputs clean Tailwind v4 @theme blocks, CSS custom properties, or DTCG JSON.

Keeping Things Valid: Google's CLI Linter

Scaffolding your configuration files is a great start, but making sure they stay valid over time is just as important. To help with this, Google built an official command-line tool you can run locally or plug straight into your CI/CD pipelines.

Just run this in your terminal:

npx @google/design.md lint DESIGN.md

The linter will parse your file directly against the spec and spit out detailed JSON findings. It instantly catches issues like broken token references, bad WCAG contrast ratios, missing layout sections, or incorrect ordering—saving you from feeding malformed rules to your AI assistants.

The Bottom Line

At the end of the day, AI coding assistants are only as good as the context you feed them. If you let every model run wild on its default web data, your team will end up wasting hours cleaning up conflicting commands, mismatched designs, and broken dependencies.

By treating AI behavior as code—checking deterministic files like AGENTS.md and DESIGN.md straight into Git—you establish a single source of truth that every model can read and respect. Start with visual generators to save time on the boilerplate, run a quick lint to keep things valid, and you’ll have a standard, consistent codebase that both humans and AI can build on seamlessly.