Add a Development Guide on Skill Layering and Splitting for Enhanced Capacity and Context Management

[strainhzj]

Hello,

I'm writing to propose a feature for the development roadmap: the addition of a new module or documentation guide on "Skill Documentation Layering and Splitting."

Problem Statement:

The official recommendation advises keeping skill files under 500 lines. This is a good practice for maintainability and clarity. However, as applications become more complex, developers will inevitably create skills that encapsulate a large amount of knowledge and logic, exceeding this limit. This leads to several issues:

1.Information Overload: A single, monolithic skill file becomes difficult for the AI to parse effectively, increasing the chance of it missing or misinterpreting key information.
2.Context Inefficiency: When a large skill is called, its entire content is loaded into the context, consuming valuable tokens even if only a small portion is relevant to the immediate task.
3.Poor Maintainability: Large files violate the single-responsibility principle, making the skill difficult to debug, update, and manage.

Proposed Solution:

To address this, I suggest adding official guidance on how to architect complex skills using a layering and splitting methodology. This approach involves:

1.Splitting: Breaking down a large, complex skill into multiple smaller, single-purpose sub-skills. For example, a travel_planner skill could be split into flight_booking, hotel_reservation, and itinerary_generation skills.
2.Layering: Creating a "master" or "index" skill that acts as a dispatcher. This master skill contains the high-level logic and instructions to understand the user's intent and then calls the appropriate sub-skill to perform the specific task.
Benefits:

Enhanced Information Capacity: This architecture allows developers to build skills with a scope and depth far beyond the 500-line guideline, enabling more sophisticated applications.
Improved Context Management: By loading only the relevant sub-skill into the context for a given task, we significantly reduce token usage and improve the accuracy and relevance of the AI's responses.
Better Organization and Maintainability: The codebase becomes more modular, organized, and easier to manage, aligning with standard software engineering principles.
Scalability: It provides a clear and scalable pattern for building enterprise-level AI agents.
For a detailed explanation and practical examples of this approach, please refer to this excellent article: https://www.bestblogs.dev/article/1e89d7ee.

I believe that adding official documentation and best practices on this topic would be an invaluable resource for the developer community and would significantly empower them to build more powerful and robust applications.

Thank you for considering this suggestion.