Don't write agent skills from scratch. First, manually guide the agent through a workflow step-by-step. After a successful run, instruct the agent to review that conversation history and generate the skill from it. This provides the crucial context of what a successful outcome looks like.

The "Agent Skills" format was created by Anthropic to solve a key performance bottleneck. As capabilities were added, system prompts became too large, degrading speed and reliability. Skills use "progressive disclosure," loading only relevant information as needed, which preserves the context window for the task at hand.

Instead of preloading all context into a single system prompt, modern agentic systems use file-based skills. The agent references a directory and loads only the specific instructions or resources relevant to a task, improving efficiency, scalability, and reusability.

Instead of asking an AI for a one-off task, identify recurring workflows and have the AI turn them into a "skill." This creates a reusable asset that dramatically improves efficiency and output quality over time, turning the user into a system builder.

Simply giving an AI agent thousands of tools is counterproductive. The real value lies in an 'agentic tool execution layer' that provides just-in-time discovery and managed execution to prevent the agent from getting overwhelmed by its options.

To scale your use of AI agents, move beyond single-use builds. Identify recurring capabilities and package them as reusable 'skills.' This modular approach makes your work transportable, allowing you to easily apply successful processes across different projects and agents, which compounds your efficiency over time.

Expecting employees to author perfect, complex prompts from scratch leads to paralysis. A better method is letting them complete a task via iteration with the AI, then having the system automatically capture those adjustments as a reusable workflow or 'skill.'

Treat AI 'skills' as Standard Operating Procedures (SOPs) for your agent. By packaging a multi-step process, like creating a custom proposal, into a '.skill' file, you can simply invoke its name in the future. This lets the agent execute the entire workflow without needing repeated instructions.

Instead of loading large context files on every turn, use "skills." The agent only sees a skill's name and description initially, loading the full instructions only when needed. This method, called progressive disclosure, drastically saves tokens and improves performance.