Claude Skills Aren't Just for Claude - Here's How to Build Them for ANY Agent

Anthropic Skills & Building Custom AI Agents: A Detailed Summary

Key Concepts:

• Skills: A method for providing AI agents with capabilities on an as-needed basis, avoiding overwhelming the context window.
• Progressive Disclosure: The core principle behind skills – revealing capabilities only when the agent requires them.
• Skill.md: The primary file containing instructions and details for a specific skill.
• Dynamic System Prompt: A system prompt that is constructed at runtime, incorporating skill descriptions.
• Evals (Evaluations): Automated testing to verify an agent’s behavior and skill utilization.
• Observability: Monitoring and analyzing agent behavior in production using tools like Logfire.
• MCP Servers: A traditional method of providing tools to agents, often resulting in context window overload.
• Pydantic AI: The agent framework used in the demonstration, but the concepts are universally applicable.

1. The Power and Simplicity of Skills

The video centers on Anthropic’s “skills” – a recent advancement in AI agent design emphasizing simplicity and efficiency. The core problem skills address is preventing context window overload in Large Language Models (LLMs). Traditional methods like MCP servers load all potential tools upfront, even if unused, overwhelming the LLM. Skills, conversely, employ progressive disclosure, initially providing only a description of a capability. The agent only accesses the full instructions (contained in a skill.md file) when a user request necessitates it. This approach is more flexible and context-efficient. The speaker emphasizes that this concept isn’t exclusive to Anthropic’s ecosystem and can be implemented with any LLM or agent framework. As stated, “We are not limited to the Claude ecosystem to take advantage of skills.”

2. Skill Structure & Progressive Disclosure Layers

A skill is structured in three layers of information disclosure:

• Layer 1: Skill Description (in System Prompt): A concise (50-100 words) description of the skill’s purpose, loaded into the system prompt upfront. This acts as a “hint” to the agent.
• Layer 2: Skill.md: The main instruction file containing detailed instructions for the capability. Typically 300-500 lines long.
• Layer 3: Reference Files: Optional supplementary documents (scripts, markdown files) providing more specific context, referenced within the skill.md. This allows for even deeper, on-demand information retrieval.

This layered approach minimizes initial context usage while allowing the agent to access detailed instructions when needed. The speaker highlights that loading all this information upfront could consume “tens of thousands of tokens,” significantly impacting performance.

3. Implementing Skills with Pydantic AI (and Universal Applicability)

The video demonstrates building skills into a custom AI agent using Pydantic AI. However, the principles are transferable to other frameworks like Langchain, Crew AI, or even a framework-less implementation. The key components are:

• Skills Directory: A folder containing individual skill folders, each with a skill.md file.
• Dynamic System Prompt: A Python function dynamically constructs the system prompt by extracting descriptions from the skill.md files in the skills directory. This ensures the agent is aware of available skills.
• Tool Set: Two primary tools are used:
  • load_skill: Takes the skill path as input and returns the content of the skill.md file, adding it to the agent’s context.
  • read_reference: Loads and returns the content of a reference file associated with a skill, providing deeper context when needed.

The speaker emphasizes the simplicity of this setup: “It’s beautifully simple, right? Simple but powerful.”

4. Best Practices for Skill Creation (Based on Anthropic’s Guide)

Drawing from Anthropic’s documentation (linked in the description), the speaker outlines best practices:

• Skill Description Length: Keep descriptions concise (50-100 words) to minimize initial context usage.
• Skill.md Length: Aim for 300-500 lines, adjusting based on complexity.
• Reference Files: Utilize reference files for specialized information, avoiding unnecessary context loading.
• Utilize Claude Desktop: Anthropic’s Claude Desktop can assist in skill creation, leveraging its built-in skill creator tool.

5. Ensuring Reliability: Evals & Observability

The speaker stresses the importance of verifying skill functionality, especially with numerous skills. He introduces two key concepts:

• Evals (Evaluations): Automated testing using a dataset of questions and expected tool calls (skill usage). Pydantic AI provides a built-in evaluation framework. This allows for rapid testing after any changes to the system prompt or skills.
• Observability (with Logfire): Monitoring agent behavior in production using Logfire, a tool created by the Pydantic team. Logfire provides insights into token usage, cost, and the agent’s decision-making process, enabling identification and resolution of issues. “Observability is so important… being able to see our usage, token usage, and cost in production.”

6. Code Demonstration & Template Availability

The video includes a live demonstration of the agent in action, showcasing its ability to leverage different skills based on user requests. The speaker also highlights the availability of a GitHub template (linked in the description) containing the code and example skills, facilitating easy replication and customization.

Conclusion:

The video provides a comprehensive guide to implementing Anthropic’s “skills” concept in custom AI agents. It emphasizes the benefits of progressive disclosure for context management, outlines a practical implementation using Pydantic AI (while stressing universal applicability), and highlights the importance of rigorous testing (evals) and monitoring (observability) for reliable performance. The key takeaway is that skills represent a powerful and relatively simple technique for building more flexible, efficient, and scalable AI agents. The provided template and resources empower viewers to immediately apply these concepts to their own projects.