How to build Claude-style skills for any AI agent

Claude's skills feature changed how we think about AI capabilities. But here's what most people don't realize: you don't need Claude to build this kind of modular, intelligent system. You can create the same progressive disclosure approach for any AI agent you're working with.

The beauty of Claude's skills isn't the underlying technology - it's the elegant simplicity of how they're structured. And that structure is something you can replicate across different AI platforms, whether you're building custom agents, working with GPT models, or developing your own AI tools.

What makes Claude skills so powerful?

Claude skills work because they solve a fundamental problem: information overload. Traditional AI agents get buried under massive system prompts filled with every possible instruction. Skills break this down into digestible, contextual pieces.

Think of skills as specialized toolkits. Instead of giving your agent a 10,000-word instruction manual upfront, you give it focused, modular capabilities that activate when needed. Each skill contains:

• A clear description of what it does
• Step-by-step instructions for execution
• Context about when to use it
• Examples and edge cases

The genius is in the layered approach. The agent sees just enough information to know what's available, then dives deeper only when a specific skill is needed.

How does progressive disclosure work in practice?

Progressive disclosure is the secret sauce that makes skills manageable. It works in three distinct layers:

Layer 1: The skill catalog - Your agent sees a simple list of available capabilities with brief descriptions. This is like scanning a toolbox to see what's inside.

Layer 2: The skill details - When a relevant skill is identified, the agent loads the full instruction set. This includes detailed steps, parameters, and usage guidelines.

Layer 3: Supporting documentation - For complex skills, additional context documents provide examples, troubleshooting, and advanced techniques.

This layered approach prevents cognitive overload while ensuring the agent has access to deep expertise when needed.

What's the basic architecture you need?

Building skills for any AI agent starts with a simple file structure. You'll want to create a standardized format that your agent can parse and understand.

Here's a basic template that works across different AI platforms:

skills/
├── skill-name/
│   ├── skill.md (main instruction file)
│   ├── examples.md (usage examples)
│   └── context.md (additional reference material)

Each skill.md file should follow a consistent format:

• Purpose: What this skill accomplishes
• When to use: Specific triggers or contexts
• Instructions: Step-by-step process
• Parameters: Required inputs and outputs
• Examples: Real usage scenarios

The key is making these files self-contained yet interconnected. Your agent should be able to understand each skill independently while seeing how they work together.

How do you teach an AI agent to use skills?

Large language models don't naturally understand modular capabilities. You need to explicitly teach them how the skills system works through clear meta-instructions.

Start with a system prompt that explains the concept:

"You have access to specialized skills that enhance your capabilities. Each skill is a focused toolkit for specific tasks. When you encounter a request, first check if any available skills are relevant. Load the appropriate skill instructions and follow them precisely."

The agent needs to understand:

• How to recognize when a skill applies
• How to load and interpret skill instructions
• How to combine multiple skills when needed
• How to handle cases where no specific skill exists

This meta-knowledge is crucial. Without it, your agent might ignore the skills entirely or use them incorrectly.

What skills should you build first?

Start with skills that address your most common use cases. Don't try to build everything at once - focus on high-impact capabilities that you'll use repeatedly.

Good starter skills include:

• Data analysis: Processing and interpreting structured data
• Content generation: Creating specific types of content with consistent formatting
• Code review: Analyzing code for specific patterns or issues
• Research synthesis: Gathering and summarizing information from multiple sources
• Quality assurance: Checking outputs against specific criteria

Build these incrementally. Start with basic functionality, then add complexity as you see what works and what doesn't.

How do you handle skill interactions?

The real power emerges when skills work together. Your agent needs to understand how to chain capabilities and when to combine different skill sets.

Create guidelines for skill orchestration:

• Sequential skills: When one skill's output feeds into another
• Parallel skills: When multiple skills can work on different aspects simultaneously
• Conditional skills: When skill selection depends on intermediate results
• Fallback skills: When primary skills fail or aren't applicable

Document these interaction patterns just like individual skills. Your agent should understand not just what each skill does, but how they connect.

What about evaluation and observability?

This is where most people stumble. Building skills is one thing - knowing if they work correctly is another entirely.

Set up evaluation frameworks that test:

• Skill selection accuracy: Does the agent choose the right skills?
• Execution quality: Does it follow the skill instructions correctly?
• Output consistency: Do similar inputs produce similar results?
• Error handling: How does it respond when skills fail?

Create test cases for each skill with known good inputs and expected outputs. Run these regularly as you modify or expand your skill library.

Observability means tracking what happens in real usage:

• Which skills get used most often?
• Where do skills fail or produce unexpected results?
• How do users respond to skill-generated outputs?

This feedback loop is essential for continuous improvement.

Which platforms work best for custom skills?

You can implement this pattern across different AI platforms, though each has its strengths:

OpenAI's GPT models work well with function calling mechanisms. You can structure skills as functions with clear signatures and documentation.

Local models like Llama give you complete control over the implementation. You can build sophisticated skill routing and management systems.

Agent frameworks like LangChain or CrewAI provide built-in patterns for tool usage that map naturally to skills.

Custom applications let you implement exactly the skill system you need without platform constraints.

The core principles remain the same regardless of platform: modular design, progressive disclosure, and clear instruction formats.

How do you scale a skills-based system?

As your skill library grows, organization becomes critical. Implement categorization, versioning, and dependency management early.

Consider creating skill families - groups of related capabilities that work together. This makes it easier for both agents and humans to understand what's available.

Version your skills explicitly. When you update instructions or add new capabilities, track the changes. This helps with debugging and ensures consistency across different agent deployments.

Build tools for skill management: ways to add, modify, remove, and test skills without disrupting the overall system. The easier it is to maintain your skill library, the more likely you are to keep it current and useful.

The future belongs to AI agents that can adapt and specialize dynamically. By building your own skills system, you're not just copying Claude - you're creating the foundation for truly intelligent, modular AI capabilities that can grow with your needs.