Claude Skills - the SOP for your agent that is bigger than MCP

Key Concepts

• Agent Skills: A combination of prompt instructions and a list of assets/tools (predefined functions, templates, guidelines) to teach an agent how to perform specific tasks, aiming for consistent results.
• Skill.md: The essential file within a skill, containing a short description of when to use the skill, which is added to the agent's context.
• MCP (Multi-tool Calling Protocol): A method for extending agent capabilities by connecting them with new tools.
• Token Consumption: The amount of data an agent's context can hold, which can be significantly reduced by using skills compared to MCPs.
• Self-Improving Agents: Agents that can learn and adapt by creating skills for their own codebase.

Agent Skills: A Powerful New Paradigm

The video introduces "Agent Skills" as a novel and potentially more impactful concept than MCPs for extending agent capabilities. Agent skills are described as a fusion of prompt instructions, which guide the agent on how to perform a task, and a curated set of assets and tools. These assets can include predefined functions, templates, and specific guidelines designed to ensure consistent output. While tools and assets are optional, a skill can be as simple as a single, well-crafted prompt.

Structure and Components of a Skill

Every skill begins with a skill.md file. This file contains a concise description that informs the agent when to utilize the skill. This description is consistently appended to the agent's context, allowing the agent to understand its available skill set. When the agent decides to invoke a skill, the remaining context within the skill file is loaded.

For more intricate skills, additional resources can be incorporated. For instance, a skill designed to generate algorithmic art might include example implementations. These examples serve as references for the agent, promoting more consistent results. Crucially, skills can also integrate predefined functions. The "Slack Gift Creator" skill exemplifies this, importing necessary packages and defining functions that instruct the agent on how to create a gift directly.

Skills vs. MCPs: A Comparative Analysis

The speaker posits that skills may surpass MCPs in utility due to several key advantages:

• Token Efficiency: MCPs can be token-intensive. Each MCP can bundle multiple tools, each with its own description, input schema, and usage guidelines. All these tokens are loaded into the agent's context, regardless of their immediate relevance. In contrast, skills are designed to consume significantly fewer tokens while enabling more complex task execution.
  • Example: The default "Chassis" MCP, with seven tools, consumes approximately 4,200 tokens. The speaker estimates that converting this into a "Chassis Skill" could reduce token consumption to as little as 70 tokens. This efficiency allows agents to be equipped with a greater number of skills and function effectively "out of the box."
• Ease of Use and Composability: MCPs often require detailed instructions on the order of tool usage, especially when tools are designed to be modular and reusable. This can lead to complex setup procedures. Skills, with their integrated skill.md providing referencing instructions, aim to simplify this process.

Real-World Applications and Examples

The video showcases practical demonstrations of agent skills:

• Slack Gift Creator:
  • Process: When asked to create a gift for a "daily stand-up time" on Slack, the agent invokes the slack_gift_creator skill. This involves loading the skill's context, generating Python code, and executing it to create the gift.
  • Underlying Mechanism: This skill leverages the command infrastructure, reusing predefined functions to generate the gift.
• Algorithmic Art Generator:
  • Process: Upon a request for "animated Zen style mountain algorithm art," the agent calls the algorithm_art skill. This skill first generates an MD file to plan the artwork, reads a template file for reference, and then produces the animated art using p5.js.
• Self-Improving Codebase Skills:
  • Scenario: The speaker demonstrates creating skills for their own codebase, specifically for a cloud platform with a frontend and shared packages.
  • Process:
    1. A "skill creator skill" is loaded.
    2. The agent is prompted to investigate the frontend conventions and identify best practices for adding new UI components.
    3. After an investigation, a "frontend" skill is created, encapsulating these best practices. This skill includes a description, guidelines for UI component implementation, and reference files for component and style guides.
    4. Subsequently, when asked to create a new UI component (e.g., for an emoji and image picker), the agent first utilizes the "frontend" skill to retrieve best practices and coding conventions before proceeding with the component's development. This enables continuous improvement of the agent's capabilities within the specific codebase.

The "Awesome Cloud Skills" Repository

The speaker highlights a personal repository named "awesome_cloud_skills," which initially contains official cloud skills but is being expanded with custom additions like "UI Design." This repository is presented as a community effort, encouraging contributions and pull requests.

Further Learning

For those interested in a deeper dive into cloud skills, the speaker announces an upcoming weekly workshop at "AI Builder Cup," with a link provided for registration.