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Rajesh Royal
Rajesh Royal

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Agent Skills: Teach Claude Once, Use Everywhere

#tutorial #claudecode #productivity #beginners

Packaged knowledge that transforms generic AI into your team's specialized expert.

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Introduction

Every team has their way of doing things. Your deployment process. Your code review checklist. Your specific testing framework quirks. Your company's component naming conventions.

Generic AI assistants don't know these things. They give generic answers that you then have to adapt to your context. Again and again. For every new developer. For every new project.

What if you could teach Claude your team's practices once, package that knowledge, and reuse it everywhere?

That's exactly what Agent Skills do. Skills are folders containing instructions, scripts, and resources that teach Claude how to perform specialized tasks — your way. And here's the exciting part: Agent Skills are now an open standard, meaning they work not just in Claude Code, but across any AI tool that adopts the standard.

The Problem

AI coding assistants are remarkable generalists. Ask Claude to write a React component, and it'll give you a solid, idiomatic component. But will it use your team's preferred patterns? Will it follow your naming conventions? Will it integrate with your specific state management approach?

Usually not. At least, not without extensive prompting every single time.

This creates several painful patterns:

The Long Prompt Tax: Every complex request requires paragraphs of context about how your team does things.

The Onboarding Bottleneck: New team members don't know the patterns, and neither does their AI assistant. Knowledge transfer is slow and incomplete.

The Inconsistency Spiral: Different developers give different context, resulting in inconsistent AI outputs across the codebase.

The Tribal Knowledge Trap: Critical "how we do things" information lives in people's heads, not in a format AI can use.

What you need is a way to encode institutional knowledge in a format that AI can understand and apply automatically.

The Solution

Agent Skills package your team's knowledge into portable, reusable folders that Claude can learn from instantly.

How to Use It

A skill is simply a folder with a specific structure:

my-skill/
├── SKILL.md           # Main instructions
├── examples/          # Reference implementations
│   ├── good-example.ts
│   └── bad-example.ts
├── templates/         # Reusable templates
│   └── component.tsx
└── scripts/           # Helper scripts
    └── validate.sh
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SKILL.md is the core — a markdown file that teaches Claude how to perform a task:

# Skill: Create Frontend Components

## Context
This skill teaches how to create React components following our team standards.

## Instructions
When creating a React component:
1. Use functional components with TypeScript
2. Place in src/components/{ComponentName}/
3. Create three files: index.tsx, styles.module.css, {ComponentName}.test.tsx
4. Use CSS Modules for styling, never inline styles
5. Export from a barrel file

## Examples
See examples/GoodComponent.tsx for our standard pattern.
See examples/BadComponent.tsx for anti-patterns to avoid.

## Validation
Run scripts/validate.sh to check compliance.
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Using skills in Claude Code:

You: @skills/frontend-component Create a UserAvatar component

Claude: [Reads skill instructions]
        [Creates src/components/UserAvatar/index.tsx]
        [Creates src/components/UserAvatar/styles.module.css]
        [Creates src/components/UserAvatar/UserAvatar.test.tsx]
        [Runs validation script]

        Created UserAvatar component following team standards.
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Pro Tips

📦 Skill inheritance: Skills can reference other skills. Create a base "React Component" skill and extend it for specific component types.

🔗 Share across projects: Since skills are just folders, you can npm publish them, add to a monorepo, or share via git submodules.

✅ Include validation: Add scripts that verify Claude followed the skill correctly. Automated quality gates!

📚 Document anti-patterns: Showing what not to do is often as valuable as showing correct patterns.

🌍 Open standard = portability: Skills work wherever the Agent Skills standard is implemented. Your knowledge investment isn't locked to one tool.

Example: Comprehensive API Endpoint Skill

api-endpoint-skill/
├── SKILL.md
├── examples/
│   ├── users-endpoint.ts       # Full working example
│   └── common-mistakes.md      # What to avoid
├── templates/
│   ├── endpoint.ts.template
│   ├── test.ts.template
│   └── docs.md.template
└── scripts/
    ├── generate-openapi.sh
    └── run-integration-tests.sh
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Real-World Use Case

Scenario: A growing startup needs to maintain consistency as the team scales from 5 to 50 developers.

The Skill Library:

.skills/
├── frontend-component/      # React component creation
├── api-endpoint/           # REST endpoint patterns
├── database-migration/     # Schema change process
├── pr-description/         # Pull request formatting
├── incident-response/      # On-call runbook execution
└── security-review/        # Security checklist automation
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Impact:

  • New developer onboarding: Day 1, they have access to the same AI-powered knowledge as 5-year veterans
  • Consistency: Every component, endpoint, and migration follows the same patterns
  • Knowledge preservation: When senior developers leave, their expertise remains encoded in skills
  • Continuous improvement: Skills evolve with the codebase, updated based on retrospectives

Usage example:

New Developer: @skills/api-endpoint Create a product catalog endpoint

Claude: [Applies team's API patterns]
        [Generates endpoint with correct auth, validation, error handling]
        [Creates tests using team's testing framework]
        [Generates OpenAPI documentation]
        [Runs integration tests]

        Created /api/products endpoint. All tests passing.
        Documentation updated at docs/api/products.md
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The new developer just created a production-ready endpoint following all team conventions — on their first day.

The Open Standard Advantage

Agent Skills being an open standard is a big deal. It means:

  1. No vendor lock-in: Your skills work in any tool that adopts the standard
  2. Community sharing: Public skill libraries emerge for common patterns
  3. Tool interoperability: Different AI tools can share the same skills
  4. Future-proofing: Your knowledge investment persists as tools evolve

You're not just teaching Claude — you're creating portable institutional knowledge that transcends any single AI tool.

Conclusion

Agent Skills represent a fundamental shift in how we work with AI coding assistants. Instead of repeatedly explaining how your team works, you encode that knowledge once and reuse it forever.

The implications are profound:

  • Onboarding becomes instant
  • Consistency becomes automatic
  • Best practices become enforceable
  • Institutional knowledge becomes permanent

Generic AI is powerful. AI that knows exactly how your team works is transformative.

And because skills are an open standard, you're not just investing in Claude Code — you're investing in portable knowledge infrastructure that will serve your team for years to come.

Coming up on Day 21: We'll explore Claude's multi-modal capabilities — working with images, diagrams, and visual content. Because sometimes a picture really is worth a thousand words of explanation.

This article is part of the "31 Days of Claude Code Features" series. Follow along to discover one powerful feature each day.

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