To prevent context overload as your foundational layer grows, each file should include a header that tells an AI skill when to use it. The skill then scans and loads only the relevant files for a given task. This ensures the AI has the right context without getting confused by irrelevant information.

Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.

An effective AI second brain is a personalized operating system, not a generic tool. This is achieved through "routing logic," where you define what information the AI should look for—like blockers, decisions, or opportunities—and how to process it.

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.

To keep your AI agent efficient, differentiate between global and project-level skills and context files. General-purpose tools, like a text truncation skill, should be global. Specific processes, like a referral template, should be kept at the project level to avoid cluttering every interaction.

Instead of overloading the context window, encapsulate deep domain knowledge into "skill" files. Claude Code can then intelligently pull in this information "just-in-time" when it needs to perform a specific task, like following a complex architectural pattern.

Seemingly complex features like long-term memory and skill creation are fundamentally clever systems for managing an AI's limited context window. The "harness" efficiently loads and unloads relevant information (memories, skills) at the precise moment it's needed, rather than keeping it all in context constantly.

M0 organizes agent knowledge into two distinct layers: a high-level "Experience" summary outlining strategy and cautions, and a detailed "Skill" layer with structured operational steps. This allows an agent to load the compact strategy first and only retrieve operational details when necessary, keeping the active prompt lean and efficient.

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.