How to build Claude Skills Better than 99% of People

Skill Engineering for AI Agents: A Deep Dive

Key Concepts:

• Skills: Folders of instructions, scripts, and resources for AI agents to perform specific processes. Core component is the skill.md file (SOP).
• Skill.md: The central instruction file defining the process flow for a skill, analogous to a system prompt or custom GPT instruction set.
• Reference Files: Supporting materials enhancing skill performance – text files (examples, style guides, ICP context), assets (images, presentations), and code scripts (API calls).
• Progressive Disclosure: A method of loading only necessary information into an agent’s context window – metadata first, then process instructions, and finally reference files when needed.
• Plugins: Bundled sets of skills, commands, agents, and connectors, offering increased functionality and shareability.
• MCP (Manual Control Process) Instructions: Files detailing how to efficiently use specific tools within a skill’s process.
• Human-in-the-Loop: Incorporating human judgment and feedback into the AI workflow, crucial for context-dependent tasks.
• Monetization Potential: The emerging opportunity to create and sell valuable skills and plugins.

I. The Rise of Skill Engineering & Why It Matters

The video emphasizes the growing importance of “skill engineering” as AI agents (Cloud Code, Co-work, OpenAI, Google) become increasingly powerful. While these agents are improving, they still require specific guidance – “guard rails, context, and SOPs” – to align with unique user and business workflows. Traditional solutions like custom GPTs and projects are isolated and lack self-improvement capabilities. Deterministic automation platforms (like Zapier/Make) are effective for fixed processes but struggle with the nuance and judgment required in most day-to-day work. Skills bridge this gap, offering a balance between automation and human oversight.

The speaker predicts that mastering skill infrastructure will be crucial for productivity gains and will become a monetizable skill set, akin to software or prompt engineering. The ability to create and share skills within a business will dramatically improve onboarding, consistency, and overall operational efficiency. The ultimate vision is AI agents becoming a “single interface for doing work.”

II. What are Agent Skills? – Deconstructing the Components

Agent skills are defined as folders containing instructions, scripts, and resources that enable agents to perform tasks accurately and efficiently. The core of a skill is the skill.md file, which functions as a Standard Operating Procedure (SOP). Beyond the skill.md, skills can incorporate:

• Instructions for Knowledge Files: Specifying when and how to utilize supporting documentation.
• Tool Usage Instructions: Defining how and when to employ specific tools.
• Sub-Agent Activation: Instructions for launching specialized agents for specific sub-tasks.
• Code Execution: Utilizing code scripts (Python, JavaScript) to perform actions like API calls.

The video illustrates skill complexity with examples: a simple sales account research skill with only a skill.md file, and a more elaborate newsletter writer skill leveraging multiple reference files for context (background, ICP, voice, personality).

III. Reference Files: Fueling Skill Performance

Reference files are critical for providing skills with the necessary context. These can take several forms:

• Text Files: Example outputs, style guides, ICP details, background information.
• Assets: Images, presentations, videos, binary files – providing visual examples or data.
• Code Scripts: Python or JavaScript functions for API calls or other automated actions (e.g., the infographic skill uses a Google API call).

The speaker highlights the ability of AI agents to create MCP (Manual Control Process) documents, guiding the agent on efficient tool usage within the skill.

IV. Progressive Disclosure: Managing Context Overload

A key innovation enabling the use of thousands of skills with a single agent is “progressive disclosure.” Instead of loading all skill data into the agent’s memory at once, only the metadata (name and description) is initially stored. The skill.md file is loaded only when the skill is triggered, and reference files are loaded only when explicitly instructed by the skill. This prevents context overload and allows for scalability.

V. Skills vs. Plugins: Understanding the Relationship

The video clarifies the distinction between skills and plugins:

• Plugins: Packaged collections of skills, commands, agents, and connectors. They add complexity but offer increased functionality, shareability (e.g., sales plugin for the sales team), and version control. Plugins are evolving towards becoming more like software or SaaS products.
• Skills: The fundamental building blocks, even within plugins. Skills can be built, triggered, and accessed directly, independent of plugins.

The speaker predicts that SaaS companies will begin developing their own plugins to extend their functionalities.

VI. Building Skills: A Framework for Success

The video outlines a framework for building effective skills:

1. Process Definition: Thoroughly map out the ideal step-by-step process before prompting.
2. Knowledge Sources: Identify relevant knowledge files (context, style guides, ICP details).
3. Tool Identification: Determine which tools or software the agent needs to access.
4. Output Examples: Prepare high-quality examples of desired outputs.

The speaker emphasizes that skill building is an iterative art, similar to software engineering, requiring attention to UX (human-in-the-loop integration), context engineering (balancing context for optimal results), and continuous improvement.

A. Prompting Framework:

• Name & Trigger: Define the skill’s name and how it should be activated.
• Goal/Objective: A concise statement of the skill’s purpose.
• Connectors/APIs/MCPs: Specify required tools and instructions for their use.
• Process (Step-by-Step): Detailed instructions for each step, including human-in-the-loop points (using dynamic QA boxes) and reference file usage.
• Output Expectations: Define the desired output for each step, including requesting multiple variations for human selection.
• Rules: Establish guidelines to prevent errors and ensure consistent performance. Include instructions for self-improvement (e.g., saving approved outputs as examples).

VII. Iteration and Improvement

The speaker stresses the importance of continuous iteration. If the skill doesn’t follow the process, modify the skill.md. If it makes a general error, add a rule. If it struggles with a tool, create an MCP document. The more a skill is used, the better it becomes.

VIII. Sharing and Deployment

Skills can be shared as zip files or deployed via GitHub. Plugins, containing multiple skills, can also be shared and managed through a centralized plugin marketplace.

Notable Quote:

“Building the skill infrastructure for these agents to do specific tasks well will not only allow you and your company to become far more productive, it will also become monetizable.” – Speaker

Conclusion:

The video presents a compelling case for skill engineering as a critical skill for the future of work. By understanding the components of skills, leveraging progressive disclosure, and adopting a structured building process, individuals and businesses can unlock the full potential of AI agents and gain a competitive advantage. The emerging ecosystem of skills and plugins promises to transform how work is done, offering opportunities for both increased productivity and new revenue streams. The key takeaway is that proactive skill development is no longer optional, but essential for navigating the rapidly evolving landscape of AI-powered automation.